iCVI-ARTMAP: Using Incremental Cluster Validity Indices and Adaptive Resonance Theory Reset Mechanism to Accelerate Validation and Achieve Multiprototype Unsupervised Representations

This article presents an adaptive resonance theory predictive mapping (ARTMAP) model, which uses incremental cluster validity indices (iCVIs) to perform unsupervised learning, namely, iCVI-ARTMAP. Incorporating iCVIs to the decision-making and many-to-one mapping capabilities of this adaptive resona...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 34; číslo 12; s. 9757 - 9770
Hlavní autori: Brito da Silva, Leonardo Enzo, Rayapati, Nagasharath, Wunsch, Donald C.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptation models
Adaptive resonance theory (ART)
adaptive resonance theory predictive mapping (ARTMAP)
Algorithms
Clustering
Clustering algorithms
Decision making
Decision theory
incremental cluster validity indices (iCVIs)
Information theory
Machine learning algorithms
Mapping
Partitioning algorithms
Prototypes
Resonance
Subspace constraints
Unsupervised learning
validation
Validity
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:This article presents an adaptive resonance theory predictive mapping (ARTMAP) model, which uses incremental cluster validity indices (iCVIs) to perform unsupervised learning, namely, iCVI-ARTMAP. Incorporating iCVIs to the decision-making and many-to-one mapping capabilities of this adaptive resonance theory (ART)-based model can improve the choices of clusters to which samples are incrementally assigned. These improvements are accomplished by intelligently performing the operations of swapping sample assignments between clusters, splitting and merging clusters, and caching the values of variables when iCVI values need to be recomputed. Using recursive formulations enables iCVI-ARTMAP to considerably reduce the computational burden associated with cluster validity index (CVI)-based offline clustering. In this work, six iCVI-ARTMAP variants were realized via the integration of one information-theoretic and five sum-of-squares-based iCVIs into fuzzy ARTMAP. With proper choice of iCVI, iCVI-ARTMAP either outperformed or performed comparably to three ART-based and four non-ART-based clustering algorithms in experiments using benchmark datasets of different natures. Naturally, the performance of iCVI-ARTMAP is subject to the selected iCVI and its suitability to the data at hand; fortunately, it is a general model in which other iCVIs can be easily embedded.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3160381