论文信息 - A Fuzzy Neighborhood Model for Clustering, Classification, and Approximations

A Fuzzy Neighborhood Model for Clustering, Classification, and Approximations

A fuzzy neighborhood model for analyzing information systems having topological structures on occurrences of keywords is proposed and algorithms of clustering, classification and approximations similar to generalized rough sets are developed. Real applications include text mining and clustering of keywords on the web. An illustrative example is given.

Sadaaki Miyamoto | Satoshi Hayakawa

[1] James C. Bezdek,et al. Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[2] David G. Stork,et al. Pattern Classification (2nd ed.) , 1999 .

[3] Jerzy W. Grzymala-Busse,et al. Rough Sets , 1995, Commun. ACM.

[4] Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[5] T. Kunii,et al. Soft Computing and Human-Centered Machines , 2013, Computer Science Workbench.

[6] Dawid Weiss,et al. Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition , 2004, Intelligent Information Systems.

[7] Brian Everitt,et al. Cluster analysis , 1974 .

[8] Robert C. Kohberger,et al. Cluster Analysis (3rd ed.) , 1994 .

[9] Tsau Young Lin,et al. A Review of Rough Set Models , 1997 .

[10] Sadaaki Miyamoto,et al. Fuzzy c-means as a regularization and maximum entropy approach , 1997 .

[11] Sadaaki Miyamoto,et al. Fuzzy Sets in Information Retrieval and Cluster Analysis , 1990, Theory and Decision Library.

[12] David G. Stork,et al. Pattern Classification , 1973 .