Moreover, in association rule mining there is a set of records each of which contains some number of items and frequent items are grouped together. Classification aims at partitioning the data so that different classes or categories can be identified based on combinations of parameters.
Classification is used to classify each item in a data set into one of a predefined set of classes or groups by generating a set of grouping rules. In other words, it is used to predict group membership for data instances. The ultimate task of a classification model is to predict categorical labels the class label attributes. Clustering is an important data mining technique that locates similar objects into clusters based on some similarity. Unlike the classification model, which analyzes class-labeled training dataset, clustering analyzes data objects without using class labels.
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