Algorithms For Approximation Proc, Chester 2005 by Iske A , Levesley J (Eds)

By Iske A , Levesley J (Eds)

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She also showed how to adapt these algorithms under the ART1 framework. In [83] and [84], the ease with which ART may be used for hierarchical clustering is also discussed. Fuzzy ART (FA) benefits the incorporation of fuzzy set theory and ART [16]. FA maintains similar operations to ART1 and uses the fuzzy set operators to replace the binary operators, so that it can work for all real data sets. Computational Intelligence in Clustering Algorithms 37 FA exhibits many desirable characteristics such as fast and stable learning and atypical pattern detection.

Experts in the relevant fields interpret the data partition. Further analysis, even experiments, may be required to guarantee the reliability of extracted knowledge. The remainder of the paper is organized as follows. In Section 2, we briefly review major clustering techniques rooted in machine learning, computer science, and statistics. More discussions on computational intelligence technologies based clustering are given in Section 3 and 4. We illustrate five important applications of the clustering algorithms in Section 5.

DNA or Protein Clustering with HMMs. The result shown here is the part of the alignment of 9 globin sequences obtained from SWISS-PROT protein sequences databank. et al. utilized an agglomerative hierarchical clustering paradigm to cluster all protein sequences in SWISSPROT [69]. In contrast with the proximity-based methods, Guralnik and Karypis transformed protein or DNA sequences into a new feature space, based on the detected sub-patterns working as the sequence features, and clustered with the K-means algorithm [36].

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