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 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.


Electrical and Computer Engineering

Publication Status

Early Access

Keywords and Phrases

Adaptation Models; Adaptive Resonance Theory (ART); Adaptive Resonance Theory Predictive Mapping (ARTMAP); Clustering; Clustering Algorithms; Incremental Cluster Validity Indices (ICVIs); Machine Learning Algorithms; Merging; Partitioning Algorithms; Prototypes; Subspace Constraints; Validation.

International Standard Serial Number (ISSN)

2162-2388; 2162-237X

Document Type

Article - Journal

Document Version


File Type





© 2022 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

30 Mar 2022