![]() the data object whose class label is well known. The Derived Model is based on the analysis set of training data i.e. Its objective is to find a derived model that describes and distinguishes data classes The list of functions involved in these processes are as follows −Ĭlassification − It predicts the class of objects whose class label is unknown. The derived model can be presented in the following forms − This derived model is based on the analysis of sets of training data. The purpose is to be able to use this model to predict the class of objects whose class label is unknown. Classification and PredictionĬlassification is the process of finding a model that describes the data classes or concepts. Group of objects that are very similar to each other but are highly different from the objects in other clusters. Mining of ClustersĬluster refers to a group of similar kind of objects. It is a kind of additional analysis performed to uncover interesting statistical correlationsīetween associated-attribute-value pairs or between two item sets to analyze that if they have positive, negative or no effect on each other. Sold with bread and only 30% of times biscuits are sold with bread. This process refers to the process of uncovering the relationship among data and determining association rules.įor example, a retailer generates an association rule that shows that 70% of time milk is Purchasing a camera is followed by memory card.įrequent Sub Structure − Substructure refers to different structural forms, such as graphs, trees, or lattices, which may be combined with item-sets or subsequences.Īssociations are used in retail sales to identify patterns that are frequently purchased Here isįrequent Item Set − It refers to a set of items that frequently appear together, for example, milk and bread.įrequent Subsequence − A sequence of patterns that occur frequently such as This class under study is called as Target Class.ĭata Discrimination − It refers to the mapping or classification of a class with some predefined group or class.įrequent patterns are those patterns that occur frequently in transactional data. These descriptions can be derived by the following two ways −ĭata Characterization − This refers to summarizing data of class under study. Such descriptions of a class or a concept are called class/concept descriptions. For example, in a company, the classes of items for sales include computer and printers, and concepts of customers include big spenders and budget spenders. ![]() HereĬlass/Concept refers to the data to be associated with the classes or concepts. The descriptive function deals with the general properties of data in the database. Of data to be mined, there are two categories of functions involved in Data Mining − ![]() Data mining deals with the kind of patterns that can be mined. ![]()
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