Granular Computing Based Data Mining In the View of Rough Set

Granular computing (GRC) is an umbrella term to cover any theories, methodologies, techniques, and tools that make use of granules(i.e., subsets of a universe) in problem solving. The philosophy of granular computing has appeared in many fields, and it is likely playing a more and more important role in data mining. Rough set theory is a very important paradigms of granular computing, are often used to process vague information in data mining. In this chapter, based on the opinion of data is also a format for knowledge representation, a new understanding for data mining, domain-oriented data-driven data mining (3DM), is introduced at first. Its main idea is that data mining is a process of knowledge transformation. Then, the relationship of 3DM and GrC, especially from the view of rough set is discussed. Then, some examples are used to illustrate how to solve real problems in data mining using granular computing. Combining rough set theory, a flexible way for processing incomplete information systems is introduced firstly. Then, a high efficient attribute reduction algorithm is developed by translating set operation of granules into logical operation of bit strings with bitmap technology. Finally, two rule generation algorithms are introduced, and experiment results show that the rule sets generated by these two algorithms are simpler than other similar algorithms.