Data Mining Paradigms

In the process of finding novel patterns, algorithms for mining large datasets face a number of issues. We discuss the issues related to efficiency in data mining. We elaborate some important data mining tasks such as clustering, classification, and association rule mining that are relevant to the content of the book. We discuss popular and representative algorithms of partitional and hierarchical data clustering. In classification, we discuss the nearest-neighbor classifier and the support vector machine. We use both these algorithms extensively in the book. We provide an elaborate discussion on issues in mining large datasets and possible solutions. We discuss each possible direction in detail. The discussion on clustering includes topics such as incremental clustering with focus on leader and BIRCH clustering algorithms, divide-and-conquer clustering algorithms, and clustering based on intermediate representation. The discussion on classification includes topics such as incremental classification and classification based on intermediate abstraction. We further discuss frequent-itemset mining with two directions such as divide-and-conquer itemset mining and intermediate abstraction for frequent-itemset mining. Bibliographic notes contain a brief discussion on the significant research contribution in each of the directions discussed in the chapter and literature for further study.