Free Essays on Why The Concept Of Heroes Survives The Centuries

could portray the divine beings and saints of Greek and Roman folklore. They have mind boggling quality, they have ponies that fly, or are godlike. â€Å"He was insightful, he saw puzzles, and knew mystery things, . . .† is a depiction of Gilgamesh. (Sandars 12) According to Sandars â€Å" . . . the divine beings gave [Gilgamesh] an ideal body . . . invested him with excellence . . . supply... Free Essays on Why The Concept Of Heroes Survives The Centuries Free Essays on Why The Concept Of Heroes Survives The Centuries WHY THE CONCEPT OF HEROES SURVIVES THE CENTURIES For a considerable length of time, society and writing have adored and worshiped phenomenal people called ‘heroes.’ The word legend infers a few pictures: quality, dauntlessness, assurance. Legends have been a piece of human societies for a large number of years. Gilgamesh is perceived as the first superhuman, trailed by numerous others including Hercules and Odysseus, Spiderman and Rambo, George Patton and Todd Beamer. Saints in â€Å"The Iliad† by Homer, for example, Agamemnon, Achilles, and Hector, all display fearlessness, quality, and persistence. Our history books are loaded up with records of chivalrous activities. Every day broadcasts report tales about saints in varying backgrounds. Understudies frequently question why study old works of writing. One explanation is that perusers of old writing are blessed to receive the absolute most noteworthy hero stories at any point composed. Makes the tales much additionally interesting that the most suffering epic, â€Å"Gilgamesh,† is about 4,800 years of age. (Lawall, â€Å"Gilgamesh† 10). Regardless of which time, our saints share these and other certain attributes for all intents and purpose. The most unexplained quality is that of being a â€Å"common person† or â€Å"regular guy.† These characteristics clarify why the idea of social saints endures the hundreds of years. Demonstrating that history rehashes itself, some of today’s superheroes pull propensities from the incredible legends of folklore. For instance, think about Superman. He is tall and attractive. He is keen. He can likewise fly, is essentially powerful, and is the most grounded of men. These words could depict the divine beings and saints of Greek and Roman folklore. They have amazing quality, they have ponies that fly, or are undying. â€Å"He was savvy, he saw puzzles, and knew mystery things, . . .† is a portrayal of Gilgamesh. (Sandars 12) According to Sandars â€Å" . . . the divine beings gave [Gilgamesh] an ideal body . . . enriched him with magnificence . . . bless...

How Was the Universe Created?

How Was the Universe Created? The three things above are great motivation behind why we put stock in this hypothesis. All the point above demonstrates the chance of the theory of how things came to be and they all meet up so as to demonstrate it. Contentions against the theory of how things came to be Numerous individuals still dont have faith in the Big Bang hypothesis since they think it isn't right. This could be a direct result of a portion of the issues with the hypothesis itself. Numerous individuals pose a wide range of inquiries and find numerous defects in the theory of the universe's origin. A great many people acknowledge that the universe never had a start as that is the main conceivable arrangement and the most sensible explanation we know. Individuals additionally accept that the universe never had a start so it will never end and will continue extending everlastingly vastly. The most concerning issue with the hypothesis is that there isnt such solid evidence for what began the enormous detonation. As we as a whole know, to begin something or something to begin there must be something to trigger that start and to the extent we know the huge explosion didnt have a beginning. Individuals dont discover the hypothesis persuading on the grounds that they accept that such an occasion could have occurred without something activating it. With respect to certain individuals likewise accept that the beginning could have been activated by God. Furthermore, God made the entire universe and it is God who began enormous detonation. This is conceivable however not certain and hence we require proof and confirmation. Nobody is sure that God exist and numerous inquiries can be posed in the presence of God. This inquiry can't be replied; it is same as asking how was the universe made. To know the genuine truth we would need to return to time. Fred Hoyle developed a model to show that the universe was interminably old and has stayed consistent. This is known as the Steady State hypothesis. This hypothesis was considerably more worthy among the strict gatherings as was less unclear. Anyway it was likewise acknowledged by the science side since it included the extension of the universe. {13} this hypothesis appears to take a shot at both logical and the strict sides. Fred Hoyle accepted that, if the universe is growing there must be something being made up in the spaces between systems. In down to earth I think this is exceptionally clear and a basic clarification as it is same as the theory of how things came to be yet has an alternate adjustment for the extension. He presumed that just a single hydrogen molecule is sufficient in a year to keep the development running. Analysis This hypothesis can be handily tried by utilizing an inflatable. On the off chance that we put red dabs on an inflatable and, at that point blow it, we will see that the red spots are growing. In the event that we center ourselves from one red speck we will see that the further dabs are moving quicker in light of the fact that the spaces between the spots are expanding. This expansion of the hole between the dabs is corresponding to the filling of universe and the reason for the development. Other Evidence against the Big Bang hypothesis was that a portion of the worlds close to our own systems were a lot more youthful and a few cosmic systems have been found to be more established than the universe. {13} - unmistakably this perception is contending against the theory of how things came to be and demonstrates its contention by giving us genuine information. This source is extremely solid and has really altered my perspective since it unmistakably discloses to us that the Big Bang hypothesis may very well be an incorrect method to portray the roots of our universe. This shows that it is so natural to change people groups mind on the theory of the universe's origin if the contention utilizes great logical models and demonstrates completely. The proof above shows us a defect in the theory of prehistoric cosmic detonation. Furthermore, the following proof against the hypothesis is the consistent state hypothesis. The consistent state hypothesis expresses that the universe didn't have a beginning yet consistently been available. This again is a supposition; it likewise says the universe never had a beginning so subsequently it wouldnt have an end. The consistent state hypothesis isn't revealing to us that the universe is static. It takes Hubbles thought of venture into account. I think this hypothesis is as solid as the huge explosion since it considers different realities. It is simpler for researcher to have faith in this since it doesn't have a strange beginning; like the à ¢Ã¢â€š ¬Ã¥big bang㠢â‚ ¬Ã¢  hypothesis does. The creationism hypothesis is it logical? Everybody has various convictions and everybody thinks in an unexpected way. A few people have had confidence in the creation story and the possibility of God. They state that Almighty God made the entire universe remembering the life for earth. I guess you can't contend with them until you give some exceptionally solid strong proof. The hypothesis expresses that God made the sky and the earth. Anyway life was absent and the earth was vacant and indistinct. In spite of the fact that this isn't logical and doesn't have adequate proof, we can't dismiss it on the grounds that there are individuals who have confidence in this simply like individuals who put stock in the Big Bang hypothesis. http://www.kiva.net/~kls/page4.html {9} The contradiction is generally through the strict gatherings as they trust God made the universe. The Bible Genesis has given me contentions against the hypothesis of enormous detonation as it expresses the procedure where God made the universe. It expresses that god made life on earth including all the seasons, the seas, the sun, the moon, and so on {12} this site furnishes me with the Bible citations. It shows how the universe was made in an alternate perspective and as that God made the universe. This story is fundamentally a conviction and confidence of strict people groups; in spite of the fact that it is dubious they despite everything put stock in it and think it is superior to enormous detonation. This hypothesis is interesting on the grounds that with respect to the absence of steady proof it is the second most well known hypothesis after the theory of how things came to be. Anyway it doesn't have any evidence for it convictions. In spite of the fact that individuals despite everything have confidence in it and it is adequate to contend with a much clarified hypothesis, for example, the theory of the universe's origin. This citation is from the holy book, the strict book of Christians. It states à ¢Ã¢â€š ¬Ã…God made the earth. In the initial expressions of the Bible, God is unequivocally proclaimed to be the maker of the earth (Gen. 1: 1, 2). The reality God made the earth is over and over instructed all through the Bible.㠢â‚ ¬Ã¢  {12} This has been taken from the holy book. This source is dependable in a religion way. Be that as it may, it doesnt have the science behind it to back its thoughts. In any case, this source is dependable in light of the fact that it has been known for a long time and numerous individuals have confidence in it. The holy book expresses that God took 7 days to make the earth and the universe. Considering the huge populace of Christians now days I figure their contentions could be successful and can likewise influence others. à ¢Ã¢â€š ¬Ã…In Christian religious philosophy, an area of unique disclosure, Gods calling (John 6:44, 6:65) empowers individuals to comprehend Gods plan and truth. Just the individuals who experience God and have their psyches powerfully opened by God can comprehend reality in these issues. This justification limits what common researchers can realize and comprehend. Except if a researcher gets such a calling the researcher will be continually learning and always unable to go to the information on the truth㠢â‚ ¬Ã¢  {12}. This is an exceptionally solid proclamation and it clarifies what Christians have faith in and contends against the researcher and others who have convictions in the theory of how things came to be. I think this site is genuinely against any science sees in light of the fact that from the statement you can see that it is testing. Fundamentally it expresses that an individual can't know the genuine truth and just individuals who get getting from God will disco ver reality. It additionally recommends that God has given us information anyway it is smarter to confine our insight and don't remain against God as he is the main maker. William Paleys contention Stretching the issue from the above clarification, this contention can be utilized for instance and be utilized as proof. Utilizing an idea of a watch Paley said that the world is all around planned simply like a pocket watch. Everything which makes the watch work should be working appropriately and everything in a watch is planned so splendidly. Consequently on the off chance that you expel something from inside the watch, it won't work. This applies same with the universe; on the off chance that we evacuate the principal things, for example, gravity, it won't work. Subsequently, the pocket watch and the universe are equal and the two of them had a maker. Subsequently, the universe must have a maker, which is God. Hinduism There are numerous religions on the planet and they all have various convictions. All the religions are hostile to science as they all have confidence in god. For instance Hinduism. Hindus accept that god made the entire universe. Their hypothesis à ¢Ã¢â€š ¬Ã…Before time started there was no paradise, no earth and no space between. Out of nowhere, from the profundities a murmuring sound started to tremble, Om. It developed and spread, filling the vacancy and pulsating with vitality. The night had finished. Vishnu got up. Vishnus worker, Brahma anticipated the Lords order. Vishnu addressed his worker: It is a great opportunity to start. Brahma bowed. Vishnu instructed: Create the world.㠢â‚ ¬Ã¢  The world was before long bristling with life and the air was loaded up with the hints of Brahmas creation. {14} this demonstrates there isn't just a single religion that can't help contradicting the theory of the universe's origin. Anyway this is just a fantasy, even the Hindus dont ha ve confirmation for this. They can't bolster their hypothesis with proof. There are more than 270 unique religions in this world. What's more, they all have various contentions and distinctive folklore. In the wake of taking a gander at these religions I can likewise say that its not just the religions that have fantasies, even the researcher have legends The Big Bang Theory. Issues with the hypothesis à ¢Ã¢â€š ¬Ã…Static universe models fit observational information better than growing universe models.㠢â‚ ¬Ã¢  Static universe models coordinate most perceptions with no flexible cutoff points. The Big Bang can coordinate every one of the basic perceptions, however just with customizable cutoff points. The microwave foundation bodes well as the limiti

Comprehensive Guide on Data Mining (and Data Mining Techniques)

Comprehensive Guide on Data Mining (and Data Mining Techniques) © Shutterstock.com | ScandinavianStockJust hearing the phrase “data mining” is enough to make your average aspiring entrepreneur or new businessman cower in fear or, at least, approach the subject warily. It sounds like something too technical and too complex, even for his analytical mind, to understand.Out of nowhere, thoughts of having to learn about highly technical subjects related to data haunts many people. Many cave in and just opt to find other people to take care of that aspect for them. Worse, in other cases, they pay little attention to it, thinking they can get away with not having anything to do with data mining in their business.Once they try to understand what data mining really is, they will realize that it is something that cannot be ignored or overlooked, since it is part and parcel of the management of a business or organization.Businesses cannot do away with implementing or applying various business intelligence methodologies, applications and technologies in order to gather and analyze data providing relevant information about the market, the industry, or the operations of the business. It just so happens that data mining is one of the most important aspects of business intelligence.WHAT IS DATA MINING?Forget about any highly technical definition you may associate with data mining and let us look at it for the relatively simple concept that it truly is. Data mining is basically the process of subjecting available data to analysis by looking at it from different perspectives, to convert it into information that will be useful in the management of a business and its operations.A simple way to describe data mining is that it is a process that aims to make sense of data by looking for patterns and relationships, so that it can be used in making business decisions.For the longest time, many people have associated data mining with the image of a set of high-end computers utilizing equally high-end software and technology to obtain data and p rocess them. This isn’t entirely wrong, because technology is definitely a huge and integral part of data mining. However, data mining is actually a broader concept, not just limited to the use of technology and similar tools.Perhaps one of the biggest reasons why many are intimidated by the very mention and idea of data mining is the fact that it involves more than one or two disciplines. When we talk of data mining, we are talking about database management and maintenance, which automatically means the involvement or use of database software and technologies. Thus, it also often entails machine learning and heavy reliance on information science and technology.Further, the analysis of data, especially of the numerical kind, is bound to make use of statistics, which is another area that some people find complicated. This will also demand a lot in terms of visualization.In short, being involved in data mining implies dipping one’s fingers and toes in more than a few rivers, so to speak, since it entails the use or application of multiple disciplines. This is what often makes data mining a challenge in the eyes of most people.We can gain a deeper understanding of what data mining is by talking about its five major elements.Extraction, transformation and uploading of the data to a data warehouse system.Data storage and management in a database system.Data access to analysts and other users.Data analysis using various software, tools and technologies.Data presentation in a useful and comprehensible format.IMPORTANCE OF DATA MININGBusinesses, organizations and industries share the same problems when it comes to data. Either they aren’t able the find the data that they require or, even if they know where to find it, they have difficulty actually getting their hands on it. In other cases, they may have access to the data, but they cannot understand it. Worse, the data may be readily available to them, and they may be able to have comprehension of it.However, fo r some reason or another, they find that they are unable to use the data.This is where data mining comes in.The main reason why data mining is very important is to facilitate the conversion of raw data into information that, in turn, will be converted into knowledge applicable for decision-making processes of businesses.Data mining has become increasingly important, especially in recent years, when nearly all industries and sectors all over the world are facing problems on data explosion. All of a sudden, there is simply too much data, and this rapid rise in the amount of data demands a corresponding increase in the amount of information and knowledge. Thus, there is a need to quickly, efficiently and effectively process all that data into usable information, and data mining offers the solution. In fact, you could say that data mining is the solution.You will find data mining to be most often used or applied in organizations or businesses that maintain fairly large to massive databa ses. The sheer size of their databases and the amount of information contained within them require more than a small measure of organization and analysis, which is where data mining comes in. Through data mining, users are able to look at data from multiple perspectives in their analysis. It will also make it easier to categorize the information processed and identify relevant patterns, relationships or correlations among the various fields the data or information belong to.Therefore, we can deduce that data mining involves tasks of a descriptive and predictive nature. Descriptive, because it involves the identification of patterns, relationships and correlations within large amounts of data, and predictive, because its application utilizes variables that are used to predict their future or unknown values. APPLICATIONS OF DATA MININGThe application of data mining is apparent across sectors and industries.Retail and ServiceThe sale of consumer goods and services in the retail and ser vice industries results in the collection of large amounts of data. The primary purpose of using data mining in these industries is to improve the firm’s customer relationship management, its supply chain management and procurement processes, its financial management, and also its core operations (which is sales).The most common areas where data mining becomes highly effective among retail and service provider companies include:Promotion Effectiveness Analysis, where the company will gather and analyze data on past successful (and unsuccessful or moderately successful) campaigns or promotions, and the costs and benefits that the campaigns provided to the company. This will give the firm an insight on what elements will increase the chances of a campaign or promotion being successful.Customer Segmentation Analysis, where the firm will take a look at the responses of the customers â€" classified in appropriate segments â€" to shifts or any changes in demographics or some other segme ntation basis.Product Pricing, where data mining will play a vital role in the firm’s product pricing policies and price models.Inventory Control, where data mining is used in monitoring and analyzing the movements in inventory levels with respect safety stock and lot size. Lead time analysis also greatly relies on data mining.Budgetary Analysis, where companies will need to compare actual expenditures to the budgeted expenses. Incidentally, knowledge obtained through data mining will be used in budgeting for subsequent periods.Profitability Analysis, where data mining is used to compare and evaluate the profitability of the different branches, stores, or any appropriate business unit of the company. This will enable management to identify the most profitable areas of the business, and decide accordingly.ManufacturingEssentially, the areas where data mining is applied in manufacturing companies are similar to those in retail and service companies. However, manufacturing businesses also use data mining for its quality improvement (QI) initiatives, where data obtained through quality improvement programs such as Six Sigma and Kaizen, to name a few, are analyzed in order to solve any issues or problems that the company may be having with regards to product quality.Finance and InsuranceBanks, insurance companies, and other financial institutions and organizations are also actively using data mining in its business intelligence initiatives. Risk Management is generally the area where data mining is most utilized. This time, data mining is used to recognize and subsequently reduce credit and market risks that financial institutions are almost always faced with. Other risks assessed with the help of data mining include liquidity risk and operational risk.For example, banks and credit card companies use data mining for credit analysis of customers. Insurance companies are mostly concerned with gaining knowledge through claims and fraud analysis.Telecommunication and UtilitiesOrganizations engaged in providing utilities services are also recipients of the benefits of data mining. For example, telecommunication companies are most likely to conduct call record analysis. Electric and water companies also perform power usage or consumption analysis through data mining.The global popularity of cellular phones in almost all transactions has made it a playground for many hackers and security threats. This spurred Coral Systems, a Colorado-based company, to create FraudBuster, which is described to be able to “track” down the types of fraud through data mining, specifically through analysis of cellular phone usage patterns in relation to fraud.TransportIn the transport industry, it is mainly all about logistics, which is why that is the area where data mining is most applied. Thus, logistics management benefits greatly from data mining. State or government transport agencies are also using data mining for its various projects, such as road construc tion and rehabilitation, traffic control, and the like.PropertyThe real estate industry heavily relies on information gleaned from property valuations which, in turn, resulted from the application of data mining. The focus is not entirely on the bottomline or the sales. Instead, data on property valuation trends over the years, as well as comparison on appraisals, are tackled.Healthcare and Medical IndustryEvery day, researches, studies and experiments are conducted in the healthcare and medical industry, which implies that there are tons of data being generated every single day. Data mining is often an integral part of those researches and studies.STEPS IN DATA MININGData mining is a process, which means that anyone using it should go through a series of iterative  steps or phases. The number of steps vary, with some packing the whole process within 5 steps. The one below involves 8 steps, primarily because we have broken down the phases into smaller parts. For example, steps #2 th rough #5 are lumped by other sources as a single step, which they call “Data Pre-processing”.For purposes of this discussion, however, let us take each step one at a time.Step #1: Defining the ProblemBefore you can get started on anything, you have to define the objectives of the data mining process you are about to embark on. What do you hope to accomplish with the data mining process? What problems do you want to address? What will the organization or business ultimately obtain from it as benefit?Step #2: Data IntegrationIt starts with the data, or the raw tidbit about an item, event, transaction or activity.The goal is to provide the users (those who are performing data mining) a unified view of the data, regardless of whether they are from single or multiple sources.This step involves:Identification of all possible sources of data. Chances are high that the initial list of sources will be quite long and heterogeneous. Integrating these data sources will save you a lot of tim e and resources later on in the process.Collection of data. Data are gathered from the sources previously identified and integrated. Usually, data obtained from multiple sources are merged.Data integration aims to lower the potential number and frequency of data redundancy and duplications in the data set and, consequently, improve the efficiency (speed) and effectiveness (accuracy) of the data mining process.Step #3: Data SelectionAfter the first step, it is highly probable that you will be faced with a mountain of data, a large chunk of which are not really relevant or even useful for data mining purposes. You have to weed out those that you won’t need, so you can focus on the data that will be of actual use later on.Create a target data set. The target data set establishes the parameters of the data that you will need or require for data mining.Select the data. From all the data gathered, identify those that fall within the data set you just targeted. Those are the data you wil l subject to pre-processing.Step #4: Data CleaningAlso called “data cleansing” and “data scrubbing”, this is where the data selected will be prepared and pre-processed, which is very important before it can undergo any data mining technique or approach.Some data mining processes refer to data cleaning as the first of a two-step data pre-processing phase.Data obtained, in their raw form, have a tendency to contain errors, inaccuracies and inconsistencies. Some may even prove to be incomplete or missing some values. Basically, the quality of the data is compromised. It is for these reasons that various techniques are employed to “clean” them up. After all, poor or low quality data is unreliable for data mining.One of the biggest reasons for these errors is the data source. If data came from a single source, the most common quality problems that require cleaning up are:Data entry errors, mostly attributed to ‘human’ factor, or error of the person in charge of the input of data into the data warehouse. They could range from simple misspellings to duplication of entries and data redundancy.Lack of integrity constraints, such as uniqueness and referential integrity. Since there is only one source of data, there is no way of ascertaining whether the data is unique or not. In the same way, duplication and inconsistency may arise due to the lack of referential integrity.Similarly, data obtained from multiple sources also have quality problems.Naming conflicts, often resulting from the fact that there are multiple sources of the same data, but named differently. The risk is that there may be data duplication brought about by the different names. Or it could be the other way around. More than one or two sources may use the same name for two sets of data that are completely unrelated or different from each other.Inconsistent aggregating, or contradictions arising from data being obtained from different sources. Duplications of data may result to them cance ling each other out.Inconsistent timing, where data may tend to overlap among each other, resulting to more confusion. The data then becomes unreliable. For example, data on shopping history of a customer may overlap when sourced from various shopping sites or portals.Cleaning up data often involves performing data profiling, or examining the available data and their related statistics and information, to determine their actual content, quality and structure.Other techniques used are clustering and various statistical approaches. Once the data has been cleaned, there is a need to update the record with the clean version.Step #5: Data TransformationThis is considered to be the second data pre-processing step. Other authors even describe data transformation as part of the data cleaning process.Despite having “cleaned” the data, they may still be incapable of being mined. To make the clean data ready for mining, they have to be transformed and consolidated accordingly. Basically, t he source data format is converted into “destination data”, a format recognizable and usable when using data mining techniques later on.The most common data transformation techniques used are:Smoothing. This method removes “noise” or inconsistencies in data. “Noise” is defined as a “random error or variance in a measured variable. Smoothing often entails performing tasks or operations that are also performed in data cleaning, such as:Binning. In this method, smoothing is done by referring to the ‘neighborhood’ of the chosen data value, and categorically distribute them in ‘bins’. This neighborhood essentially refers to the values around the chosen data value. Sorting the values in bins or buckets will smooth out the noise.Clustering. This operation is performed by organizing values into clusters or groups, ordinarily according to a certain characteristic or variable. In short, data values that are similar will belong to one cluster. This will smooth and remove any data noise.Regression. As a method for smoothing noise in data values, linear regression works by determining the best line to fit two variables and, in the process, improve their predictive value. Multiple regression, on the other hand, also works, but involves more than two variables.Aggregation. This involves the application of summarization tactics on data to further reduce its bulk and streamline processes. Usually, this operation is used to create a data cube, which will then be used later for analysis of data. A common example is how a retail company summarizes or aggregates its sales data periodically per period. Therefore, they have data on daily, weekly, monthly and annual sales.Generalization. Much like aggregation, generalization also leads to reduction of data size. The low-level or raw data are identified and subsequently replaced with higher-level data. An example is when data values on customer age is replaced by the higher level data concept of grouping them as pre-teen, teen, middle-aged, and senior. In a similar manner, raw data on families’ annual income may be generalized and transformed into higher-level concepts such as low-level, mid-level, or high-income level families.Normalization or Standardization. Data variations and differences can also have an impact of data quality. Large gaps can cause problems when data mining techniques are finally applied. Thus, there is a need to normalize them. Normalization is performed by specifying a small and acceptable range (the standard), and scaling the data in order to ensure they fall within that range.Examples of normalization tactics employed are Min-Max Normalization, Z-Score Normalization, and Normalization by Decimal Scaling.Step #6: Data MiningData mining techniques will now be employed to identify the patterns, correlations or relationships within and among the database. This is the heart of the entire data mining process, involving extraction of data patterns using various methods and operations.The choice on which data mining approach or operation to use will largely depend on the objective of the entire data mining process.The most common data mining techniques will be discussed later in the article.Step #7: Pattern EvaluationThe pattern, correlations and relationships identified through data mining techniques are inspected, evaluated and analyzed. Evaluation is done by using “interestingness” parameters or measures in figuring out which patterns are truly interesting and relevant or impactful enough to become a body of useful knowledge.The interpretation in this stepwill formally mark the transformation of a mere information into an entire “bag of knowledge”.Step #8: Knowledge PresentationThe knowledge resulting from the evaluation and interpretation will now have to be presented to stakeholders. Presentation is usually done through visualization techniques and other knowledge representation mechanisms. Once presented, the knowledge may, or will, b e used in making sound business decisions. DATA MINING TECHNIQUESOver the years, as the concept of data mining evolved, and technology has become more advanced, more and more techniques and tools were introduced to facilitate the process of data analysis. In Step #5 of the Data Mining process, the mining of the transformed data will make use of various techniques, as applicable.Below are some of the most commonly used techniques or tasks in data mining, classified whether they are descriptive or predictive in nature.Descriptive Mining TechniquesClustering or Cluster AnalysisClustering is, quite possibly, one of the oldest data mining techniques, and also one of the most effective and simplest to perform. As briefly described earlier, it involves grouping data values that have something in common, or have a similarity, together in a meaningful subset or group, which are referred to as “clusters”.The grouping or clustering in this technique is natural, meaning there are no predefi ned classes or groups where the data values are distributed or clustered into.Perhaps the most recognizable example of clustering used as a data mining tool is in market research, particularly in market segmentation, where the market is divided into unique segments. For instance, a manufacturer of cosmetic and skin care products for females may cluster its customer data values into segments based on the age of the users. Most likely the main clusters may include teens, young adults, middle age and mature.Association Rule DiscoveryThe purpose of this technique is to provide insight on the relationships and correlations that associate or bind a set of items or data values in a large database. Analysis of data is done mostly by looking for patterns and correlations.Customer behavior is a prime example of the application of Association Rules in data mining. Businesses analyze customer behavior in order to make decisions on key areas such as product price points and product features to b e offered.Incidentally, this technique may also be predictive, such as when it is used to predict customer behavior in response to changes. For example, if the company decides to launch a new product in the market, how will the consumers receive it? Association Rules may help in making hypotheses on how the customers will accept the new product.Sequential Pattern DiscoveryThis mining technique is slightly similar to the Association Rule technique, in the sense that the focus is on the discovery of interesting relationships or associations among data values in a database. However, unlike Association Rule, Sequential Pattern Discovery considers order or sequence within a transaction and even within an organization.Sequence Discovery or Sequence Rules is often applied to data contained in sequence databases, where the values are presented in order. In the example about customer behavior, this technique may be used to get a detailed picture of the sequence of events that a customer foll ows when making a purchase. He may have a specific sequence on what product he purchases first, then second, then third, and so on.Concept or Class DescriptionThis technique is straightforward enough, focusing on “characterization” and “discrimination” (which is why it is also referred to often as the Characterization and Discrimination technique. Data, or its characteristics, are generalized and summarized, and subsequently compared and contrasted.A data mining system is expected to be able to come up with a descriptive summary of the characteristics or data values. That is the data characterization aspect.For example, a company planning to expand its operations overseas is wondering which location would be most appropriate. Should they open an overseas branch in a county that experiences precipitation and storms for a greater half of the year, or should they pick a location that is mostly dry and arid throughout the year? Data characteristics on these two regions will be l ooked into for their descriptions, and then compared (or discriminated) for similarities and differences.Predictive Mining TechniquesClassificationThis method has several similarities with Clustering, which leads many to assume that they are one and the same. However, what makes them different is how, in Classification, there are already predetermined and pre-labeled instances, groups or classes. In clustering, the clusters are defined first, and the data values are put into the clusters they belong to. In classification, there are already pre-defined groups and, of course, it in these groups where the data values will be sorted into.In Classification, the data values will be segregated to the grouping or instances and be used in making predictions on how each of the data values will behave, depending on that of the other items within the class.An example is in medical research when analyzing the most common diseases that a country’s population suffers from. The classifications of diseases are already existing, and all that is left is for the researchers to collect data on the symptoms suffered by the population and classify them under the appropriate types of diseases.Nearest Neighbor AnalysisThis predictive technique is also similar to clustering in the sense that it involves taking the chosen data value in context of the other values around it. While clustering involves data values in extremely close proximity with each other, seeing as they belong to the same cluster, the nearest neighbor is more on the nearness of the data values being matched or compared to the chosen data value.In the cosmetic and skin care product manufacturing company example cited above, this technique may be used when the company wants to figure out which of their products are the bestsellers in their many locations or branches. If Product A is the bestseller in Location 1, and Location 10 is where Product J is selling like hot cakes, then the chances are greater that Location 2, which is nearer to Location 1 than Location 10 is, will also record higher sales for Product A more than Product J.RegressionRegression techniques come in handy when trying to determine relationships dependent and independent variables. It is a popular technique primarily because of its predictive capabilities, which is why you are likely to see it applied in business planning, marketing, budgeting, and financial forecasting, among others.Simple linear regression, which contains only one predictor (independent variable) and one dependent variable, resulting to a prediction. Presented graphically, the regression model that demonstrates a shorter distance or line between the X-axis (the predictor) and the Y-axis (the prediction or data point) will be the simple linear regression model to be used for predictive purposes.Multiple linear regression, which aims to predict the value of the responses or predictions with respect to multiple independent variables or predictors. Compared to th e simple regression, this is fairly more complicated and work-intensive, since it deals with a larger data set.Regression analysis is often used in data mining for purposes of predicting customer behavior in making purchases using their credit cards, or making an estimate of how long a manufacturing equipment will remain serviceable before it requires a major overhaul or repair. In the latter example, the company may plan and budget its expenditure on repairs and maintenance of equipment accordingly, and maybe even assess the feasibility of purchasing a new equipment instead of repeatedly spending more money on maintenance of the old one.So, now here is the fun stuff (hint: its the video :-). Decision TreesWhat makes this predictive technique very popular is its visual presentation of data values in a tree. The tree represents the original set of data, which are then segmented or divided into the branches, with each leaf representing a segment. The prediction is the result of a seri es of decisions, presented in the tree diagrams as a Yes/No question.What makes this model even more preferred is how the segments come with descriptions. This versatility â€" offering both descriptive and predictive value in an easy-to-understand presentation â€" is the main reason why decision trees are gaining much traction in data mining and database management, in general.Outlier AnalysisIn instances where there are already established models or general behavior expected from data objects, data mining may be done by taking a look at the exceptions or, in this case, what we call the “outliers”. These are the data objects that do not fall within the established model or do not comply with the expected general behavior. The result of these deviations may prove to be data that can be used as a body of knowledge later on.A classic example of applying outlier analysis is in credit card fraud detection. The shopping history of a specific customer already provides an e-tailer (onli ne retail store) a set of general behavioral data to base on. When trying to find if the fraudulent purchases have been made using the credit card of that customer, the focus of the analysis will be unusual purchases in his shopping history, such as surprisingly large amounts spent on a single purchase, or the unusual purchase of a specific item that is completely unrelated to all previous purchases.If the customer, for the past three years, has made a purchase at least once in every 2 months, a single month with the customer purchasing more than two or three times is enough to raise a red flag that his credit card may have been stolen and being improperly and fraudulently used.Evolution AnalysisWhen the data to be subjected to mining inherently changes or evolves over time, and the goal is to establish a clear pattern that will help in predicting the future behavior of the data object, a recommended approach is evolution analysis.Evolution analysis involves the identification, desc ription and modeling of trends, patterns and other regularities with respect to the behavior of data objects as they evolve or change. Thus, you will often find this applied the mining and analysis of time-series data. Stock market trends, specifically on stock prices in the stock market, are subjected to time-series analysis. The output will enable investors and stock market analysts to predict the future trend of the stock market, and this will ultimately guide them in making their stock investment decisions.There are a lot of other techniques used in data mining, and we named only a few of the most popular and the most commonly used approaches. Application of these techniques also require the use of other disciplines and tools, such as statistics, mathematics, and software management.The success of a business rides a lot on how good management is at decision-making. And let us not forget that a decision will only be as good as the quality of the information or knowledge tapped in to by the decision-makers. High quality information will rely heavily on how the collection, processing and evaluation of data. If data mining was unsuccessful or less than effective in the first place, then there is a great chance that the resulting “bag of knowledge” will not be as accurate and effective as well, and poor business decisions may be arrived at.