The availability of packaged clustering programs means that anyone with data can easily do cluster analysis on it. But many users of this technology don't fully appreciate its many hidden dangers. In today's world of "grab and go algorithms," cluster analysis is very much an art as well as a science, and it is easy to stumble if due to not understanding its pitfalls. Even if you are familiar with them, it is easy to make mistakes. The parenthetical word usually in the title is important, because all clustering algorithms can and do fail from time to time.
Modern cluster analysis has become so technically intricate that it is often hard for the beginner or the non-specialist to appreciate and understand its many hidden dangers. This book describes four classical methods for clustering in small, static data sets that have all withstood the tests of time. The youngest of the four methods is now almost 50 years old:
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James C. Bezdek, PhD is a visiting senior fellow at University of Melbourne, Australia. He received his BS in Civil Engineering from the University of Nevada (Reno) in 1969; and his PhD in Applied Mathematics from Cornell University in 1973. He is past president of NAFIPS (North American Fuzzy Information Processing Society), IFSA (International Fuzzy Systems Association) and the IEEE CIS (Computational Intelligence Society when it was the Neural Networks Council); founding editor of the International Journal Approximate Reasoning and the IEEE Transactions on Fuzzy Systems; Life fellow of the IEEE and IFSA; recipient of the IEEE 3rd Millennium, IEEE CIS Fuzzy Systems Pioneer, and IEEE technical field award Rosenblatt medals; and the IPMU Kempe de Feret award.
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