Self-Adaptive Multiprototype-Based Competitive Learning Approach: A k-Means-Type Algorithm for Imbalanced Data Clustering

Class imbalance problem has been extensively studied in the recent years, but imbalanced data clustering in unsupervised environment, that is, the number of samples among clusters is imbalanced, has yet to be well studied. This paper, therefore, studies the imbalanced data clustering problem within...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 51; no. 3; pp. 1598 - 1612
Main Authors: Lu, Yang, Cheung, Yiu-Ming, Tang, Yuan Yan
Format: Journal Article
Language:English
Published: United States IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k -means-type algorithm
Algorithms
Class imbalance learning
Clustering
Clustering algorithms
Competition
competitive learning
Cybernetics
data clustering
Density measurement
internal validation measure
Learning systems
Machine learning
Merging
multiprototype clustering
Power capacitors
Prototypes
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:Class imbalance problem has been extensively studied in the recent years, but imbalanced data clustering in unsupervised environment, that is, the number of samples among clusters is imbalanced, has yet to be well studied. This paper, therefore, studies the imbalanced data clustering problem within the framework of <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-means-type competitive learning. We introduce a new method called self-adaptive multiprototype-based competitive learning (SMCL) for imbalanced clusters. It uses multiple subclusters to represent each cluster with an automatic adjustment of the number of subclusters. Then, the subclusters are merged into the final clusters based on a novel separation measure. We also propose a new internal clustering validation measure to determine the number of final clusters during the merging process for imbalanced clusters. The advantages of SMCL are threefold: 1) it inherits the advantages of competitive learning and meanwhile is applicable to the imbalanced data clustering; 2) the self-adaptive multiprototype mechanism uses a proper number of subclusters to represent each cluster with any arbitrary shape; and 3) it automatically determines the number of clusters for imbalanced clusters. SMCL is compared with the existing counterparts for imbalanced clustering on the synthetic and real datasets. The experimental results show the efficacy of SMCL for imbalanced clusters.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2019.2916196