By Kai-Uwe Sattler

ISBN-10: 3898643859

ISBN-13: 9783898643856

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Baraldi and Alpaydin proposed Simplified ART (SART) following their general ART clustering networks frame, which is described through a feed-forward architecture combined with a match comparison mechanism [4]. As specific examples, they illustrated Symmetric Fuzzy ART (SFART) and Fully Self-Organizing SART (FOSART) networks. These networks outperform ART1 and FA according to their empirical studies [4]. Like ART family, there are other neural network-based constructive clustering algorithms that can adaptively and dynamically adjust the number of clusters rather than use a pre-specified and fixed number, as K-means and SOFM require [26, 62, 65, 90].

4 Kernel-Based Clustering Kernel-based learning algorithms [60, 71, 80] are based on Cover’s theorem. By nonlinearly transforming a set of complex and nonlinearly separable patterns into a higher-dimensional feature space, we can obtain the possibility to separate these patterns linearly [41]. The difficulty of curse of dimensionality can be overcome by the kernel trick, arising from Mercer’s theorem [41]. By designing and calculating an inner-product kernel, we can avoid the time-consuming, sometimes even infeasible process, to explicitly describe the nonlinear mapping and compute the corresponding points in the transformed space.

4. 5. Function recognition of uncharacterized genes or proteins [36]; Structure identification of large-scale DNA or protein databases [69, 74]; Redundancy decrease of large-scale DNA or protein databases [52]; Domain identification [27, 35]; EST (Expressed Sequence Tag) clustering [10]. Since biology sequential data are expressed in an alphabetic form, conventional measure methods are not appropriate. If a sequence comparison is regarded as a process of transforming a given sequence to another with a series of substitution, insertion, and deletion operations, the distance between the two sequences can be defined by virtue of the minimum number of required operations, known as edit distance [37, 68].

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