Distributed Dual Vigilance Fuzzy Adaptive Resonance Theory Learns Online, Retrieves Arbitrarily-Shaped Clusters, and Mitigates Order Dependence
Abstract
This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with distributed higher-order activation and match functions and a dual vigilance mechanism. Together, these allow DDVFA to perform unsupervised modularization, create multi-prototype cluster representations, retrieve arbitrarily-shaped clusters, and reduce category proliferation. Another important contribution is the reduction of order-dependence, an issue that affects any agglomerative clustering method. This paper demonstrates two approaches for mitigating order-dependence: pre-processing using visual assessment of cluster tendency (VAT) or post-processing using a novel Merge ART module. The former is suitable for batch processing, whereas the latter also works for online learning. Experimental results in online mode carried out on 30 benchmark data sets show that DDVFA cascaded with Merge ART statistically outperformed the best other ART-based systems when samples were randomly presented. Conversely, they were found to be statistically equivalent in offline mode when samples were pre-processed using VAT. Remarkably, performance comparisons to non-ART-based clustering algorithms show that DDVFA (which learns incrementally) was also statistically equivalent to the non-incremental (offline) methods of density-based spatial clustering of applications with noise (DBSCAN), single linkage hierarchical agglomerative clustering (SL-HAC), and k-means, while retaining the appealing properties of ART. Links to the source code and data are provided. Considering the algorithm's simplicity, online learning capability, and performance, it is an ideal choice for many agglomerative clustering applications.
Recommended Citation
L. E. Brito da Silva et al., "Distributed Dual Vigilance Fuzzy Adaptive Resonance Theory Learns Online, Retrieves Arbitrarily-Shaped Clusters, and Mitigates Order Dependence," Neural Networks, vol. 121, pp. 208 - 228, Elsevier Ltd, Jan 2020.
The definitive version is available at https://doi.org/10.1016/j.neunet.2019.08.033
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Adaptive Resonance Theory; Clustering; Distributed Representation; Fuzzy; Topology; Visual Assessment of Cluster Tendency
International Standard Serial Number (ISSN)
0893-6080
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Elsevier Ltd, All rights reserved.
Publication Date
01 Jan 2020
PubMed ID
31574412
Comments
This research was sponsored by the Missouri University of Science and Technology, USA Mary K. Finley Endowment and Intelligent Systems Center; the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brazil (CAPES) - Finance code BEX 13494/13-9 ; the Army Research Laboratory (ARL), USA and the Lifelong Learning Machines program from the DARPA/Microsystems Technology Office , and it was accomplished under Cooperative Agreement Number W911NF-18-2-0260.