Case generation using rough sets with fuzzy representation

• Published in: IEEE transactions on knowledge and data engineering Vol. 16; no. 3; pp. 293 - 300
• Main Authors: Pal, S.K., Pabitra Mitra
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering algorithms; Computer aided software engineering; Data mining; Fuzzy; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Hybrid power systems; Information retrieval; Mathematical models; Representations; Retrieval; Rough sets; Set theory; Studies
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2003.1262181

We propose a rough-fuzzy hybridization scheme for case generation. Fuzzy set theory is used for linguistic representation of patterns, thereby producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules which model informative regions in the granulated feature space. The fuzzy membership functions corresponding to the informative regions are stored as cases along with the strength values. Case retrieval is made using a similarity measure based on these membership functions. Unlike the existing case selection methods, the cases here are cluster granules and not sample points. Also, each case involves a reduced number of relevant features. These makes the algorithm suitable for mining data sets, large both in dimension and size, due to its low-time requirement in case generation as well as retrieval. Superiority of the algorithm in terms of classification accuracy and case generation and retrieval times is demonstrated on some real-life data sets.