What are the functionalities of data mining?

Data mining functionalities are used to represent the type of patterns that have to be discovered in data mining tasks. In general, data mining tasks can be classified into two types including descriptive and predictive. Descriptive mining tasks define the common features of the data in the database and the predictive mining tasks act inference on the current information to develop predictions.

There are various data mining functionalities which are as follows −

  • Data characterization − It is a summarization of the general characteristics of an object class of data. The data corresponding to the user-specified class is generally collected by a database query. The output of data characterization can be presented in multiple forms.

  • Data discrimination − It is a comparison of the general characteristics of target class data objects with the general characteristics of objects from one or a set of contrasting classes. The target and contrasting classes can be represented by the user, and the equivalent data objects fetched through database queries.

  • Association Analysis − It analyses the set of items that generally occur together in a transactional dataset. There are two parameters that are used for determining the association rules −

    • It provides which identifies the common item set in the database.

    • Confidence is the conditional probability that an item occurs in a transaction when another item occurs.

  • Classification − Classification is the procedure of discovering a model that represents and distinguishes data classes or concepts, for the objective of being able to use the model to predict the class of objects whose class label is anonymous. The derived model is established on the analysis of a set of training data (i.e., data objects whose class label is common).

  • Prediction − It defines predict some unavailable data values or pending trends. An object can be anticipated based on the attribute values of the object and attribute values of the classes. It can be a prediction of missing numerical values or increase/decrease trends in time-related information.

  • Clustering − It is similar to classification but the classes are not predefined. The classes are represented by data attributes. It is unsupervised learning. The objects are clustered or grouped, depends on the principle of maximizing the intraclass similarity and minimizing the intraclass similarity.

  • Outlier analysis − Outliers are data elements that cannot be grouped in a given class or cluster. These are the data objects which have multiple behaviour from the general behaviour of other data objects. The analysis of this type of data can be essential to mine the knowledge.

  • Evolution analysis − It defines the trends for objects whose behaviour changes over some time.

Updated on: 31-Oct-2023

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