A survey of fuzzy clustering algorithms for pattern recognition. II

Abstract

For pt.I see ibid., p.775-85. In part I an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed on the basis of the existing literature. Moreover, a set of functional attributes is selected for use as dictionary entries in the comparison of clustering algorithms. In this paper, five clustering algorithms taken from the literature are reviewed, assessed and compared on the basis of the selected properties of interest. These clustering models are (1) self-organizing map (SOM); (2) fuzzy learning vector quantization (FLVQ); (3) fuzzy adaptive resonance theory (fuzzy ART); (4) growing neural gas (GNG); (5) fully self-organizing simplified adaptive resonance theory (FOSART). Although our theoretical comparison is fairly simple, it yields observations that may appear parodoxical. First, only FLVQ, fuzzy ART, and FOSART exploit concepts derived from fuzzy set theory (e.g., relative and/or absolute fuzzy membership functions). Secondly, only SOM, FLVQ, GNG, and FOSART employ soft competitive learning mechanisms, which are affected by asymptotic misbehaviors in the case of FLVQ, i.e., only SOM, GNG, and FOSART are considered effective fuzzy clustering algorithms.

On-line novelty detection for autonomous mobile robots

“…The work of Baraldi et al, known as FOSART [16,15] and the Incremental Topology Preserving Map of Millan et al [17] are conceptually similar to the novelty filter described in this paper. They consider the problem of growing neural networks through a merger of the class of simplified-ART (SART) networks and the GNG.…”

Fuzzy Clustering Algorithms and Validity Indices for Distributed Data

“…The literature on fuzzy clustering is extensive and several studies have been carried out with different characteristics and for different purposes during the past years [2,[6][7][8][9]. One of the most used fuzzy clustering algorithms is the Fuzzy c-Means (FCM) [5,10].…”

“…In this context, Fig. 5.2a shows that the clustering algorithms, except for DGK and DGG (which compute covariance matrices) 9 , spent Table 5.7 describes the amount of data transfer (in MBs) among the data sites after the execution of 10 runs of DOMR with t D 100 iterations of each clustering algorithm. These quantities are proportional to the number of iterations.…”

Multi-Objective Evolutionary Fuzzy Clustering for High-Dimensional Problems

“…In pattern recognition fuzzy models and algorithms have been widely studied and applied [25], [26], [27]. In particular one of the major techniques in pattern recognition is fuzzy clustering, that attracts attention because it has been successful in a variety of substantive areas [28], [29], [30], [31] including image recognition, signal processing, business, health, aerospace, and so on.…”