Validity index-based vigilance test in adaptive resonance theory neural networks

Silva, Leonardo Enzo Brito da; Wunsch, Donald C.

doi:10.1109/ssci.2017.8285206

Cited by 10 publications

(2 citation statements)

References 43 publications

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“…A well-known drawback of ART-based algorithms is the specification of significantly data-dependent parameters such as a similarity threshold (i.e., a vigilance parameter). Several studies have proposed to avoid and/or suppress the effect of the abovementioned drawback by applying multiple vigilance values [27], by specifying the vigilance parameter indirectly [12], [28], and by adjusting some data-dependent parameters during the learning process [29]. However, parameters to be pre-specified still exist in these algorithms, which affect their clustering performance.…”

Section: Growing Self-organizing Clustering Algorithmsmentioning

confidence: 99%

Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Masuyama¹,

Amako

Yuna

et al. 2022

IEEE Access

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Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.

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Section: Growing Self-organizing Clustering Algorithmsmentioning

confidence: 99%

Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Masuyama¹,

Amako

Yuna

et al. 2022

IEEE Access

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“…For this study's experiments, adaptive resonance theory (ART) [57] has been implemented. It is a fast and stable online clustering method with automatic category recognition encompassing a rich history with many implementations well-suited to iCVI computation [17]- [19], [57]- [72].…”

Section: Adaptive Resonance Theory (Art)mentioning

confidence: 99%

Incremental Cluster Validity Indices for Online Learning of Hard Partitions: Extensions and Comparative Study

2020

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Validation is one of the most important aspects of clustering, but most approaches have been batch methods. Recently, interest has grown in providing incremental alternatives. This paper extends the incremental cluster validity index (iCVI) family to include incremental versions of Calinski-Harabasz (iCH), I index and Pakhira-Bandyopadhyay-Maulik (iI and iPBM), Silhouette (iSIL), Negentropy Increment (iNI), Representative Cross Information Potential (irCIP) and Representative Cross Entropy (irH), and Conn Index (iConn Index). Additionally, the effect of under-and over-partitioning on the behavior of these six iCVIs, the Partition Separation (PS) index, as well as two other recently developed iCVIs (incremental Xie-Beni (iXB) and incremental Davies-Bouldin (iDB)) was examined through a comparative study. Experimental results using fuzzy adaptive resonance theory (ART)-based clustering methods showed that while evidence of most under-partitioning cases could be inferred from the behaviors of all these iCVIs, over-partitioning was found to be a more challenging scenario indicated only by the iConn Index. The expansion of incremental validity indices provides significant novel opportunities for assessing and interpreting the results of unsupervised learning. PREPRINT SUBMITTED TO ARXIV.ORG 2 I. INTRODUCTION Cluster validation [1] is a critical topic in cluster analysis. It is crucial to assess the quality of the partitions detected by clustering algorithms when there is no class label information. Different clustering solutions may be found by distinct algorithms, or even by the same algorithm subjected to different hyper-parameters or a different input presentation order [2], [3]. Cluster validity indices (CVIs) perform the role of evaluators of such solutions. CVIs typically exhibit a trade-off between measures of compactness (within-cluster scatter) and isolation (between-cluster separation) [2]. Numerous examples of such criteria have been presented in the literature; for comprehensive reviews and experimental studies the interested reader can go to [4]-[11]. Recently, incremental cluster validity indices (iCVIs) have been developed to track the effectiveness of online clustering methods over data streams [12]-[15]. To enable cluster validation in such applications, a recursive formulation of compactness was introduced in [12], [13]. This strategy has been used to develop incremental versions of four CVIs so far [15]: viz., incremental Davies-Bouldin (iDB) [12], [13], incremental Xie-Beni (iXB) [12], [13] and modified Dunn's indices [16]. Particularly, the behavior of iXB and iDB are analyzed in both accurately and poorly partitioned data sets in [12], [13], whereas the studies in [14], [15] only investigate the iDB's behavior in cases where online clustering algorithms accurately detect data structures, i.e., when they yield high performing experimental results. Therefore, the contributions of this work are three-fold: (1) presenting incremental versions of six additional CVIs (thereby extending the family of iC...

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Determining the weights of influencing factors of construction lands with a neural network algorithm: a case study based on Ya’an City

et al. 2021

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Validity index-based vigilance test in adaptive resonance theory neural networks

Cited by 10 publications

References 43 publications

Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Incremental Cluster Validity Indices for Online Learning of Hard Partitions: Extensions and Comparative Study

Determining the weights of influencing factors of construction lands with a neural network algorithm: a case study based on Ya’an City

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