Describe the various types of data mining functionalities and the kinds of patterns they can discover.

Various data mining functionalities and the kinds of patterns are explained below.

(1) Characterization and Discrimination — Refer to click here

(2) Association and Correlations —

Suppose, as a marketing manager of all electronics, you would like to determine which item are frequently purchased together within the same / transactions: An example of such a rule, mined from the AllElectronics transactional database, is

buys(X, “computer”) buys(X, “software”) [support .— 1%, confidence,= 50%]’

where X is a variable representing a customer, A confidence, or certainty, of – 50% means that if a customer buys a computer, there is a 50%.ehance that he will buy software as well. A 1% support means that -I% 9f all the transactions. Under analysis showed that computer and software were purchased together. This association rule involves a single attribute or predicates that repeats. Association rules that contain a single predicate are referred to as single-dimensional association rules.

(3) Classification and Prediction — Refer to Click here

(4) Cluster Analysis—

Cluster analysis analyzes data objects without consulting a known class label. In general, the class labels are not present in the training ‘data simply because they are not known to begin with. Clustering can be used to generate such. tables. The objects are clustered or grouped based on the principle of maximizing the interclass similarity and minimizing the interclass similarity.

That is clusters of objects are formed so that objects Within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Each cluster. that. is formed. can be viewed as a class of objects from which rules can be derived. Clustering can also facilitate taxonomy formation, that is, the, organization of observation into a hierarchy of classes that group similar events together.

Cluster analysis can be performed on All electronics customer data in order to identify homogeneous subpopulations of customers. These clusters may represent individual target groups for marketing. a 2-D plot of customers with respect to customer’s locations, in a city. Three clusters of data points are evident.

(5) Outlier. Analysis —

A database may contain:6* objects that do not comply with the general behavior or model of the data. these. data objects are outliers. Most data mining methods discard outliers as noise or exceptions However, in some applications such as fraud detection, the rare events can be more interesting than the more regularly occurring ones. The analysis of outlier data is referred to as outlier mining.

Outliers may be detected using statistical tests that assume distribution or probability. model for the data, or using distance measures where objects are a substantial distance from any other cluster are considered outliers. Rather than using statistical or distance measures, deviation-based methods identify outliers by examining .differences in the main characteristics of objects in a group.

Outlier analysis may uncover fraudulent usage of credit cards by detecting purchases of extremely large amounts for a given account number in comparison to regular charges incurred. by the same account. Outlier values may also be detected with respect to the location and type of purchase, or the. purchase frequency.

Evolution Analysis –

Data evolution analysis describes and models regularities or trends for objects whose behavior changes over time. Although this may include characterization, discrimination, correlation analysis. classification, prediction, or clustering of time. data, distinct features of such an analysis include time-series data analysis, sequence or periodicity pattern matching, and similarity-based data analysis.

Suppose that you have the major stock market (time-series) data of the last several years available from the New York Stock Exchange and you would like to invest in shares of high-tech industrial companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing to your decision-making regarding stock investments.

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