Fuzzy K-means clustering with reconstructed information

Clustering techniques play a pivotal role in unveiling the inherent structure of unlabeled data. When dealing with overlapping clusters, traditional hard clustering methods encounter challenges. As a representative of soft clustering methods, Fuzzy K-Means (FKM) enables data points to be assigned di...

Full description

Saved in:
Bibliographic Details
Published in:International journal of machine learning and cybernetics Vol. 16; no. 1; pp. 43 - 53
Main Authors: Huang, Honglan, Shi, Wei, Yang, Fangjie, Feng, Yanghe, Zhang, Longfei, Liang, Xingxing, Shi, Jun, Cheng, Guangquan, Huang, Jincai, Liu, Zhong
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Cluster analysis
Clustering
Complex Systems
Computational Intelligence
Control
Data points
Data structures
Datasets
Decision making
Dimensional analysis
Eigenvectors
Engineering
Mechatronics
Methods
Optimization
Original Article
Pattern Recognition
Performance evaluation
Robotics
Systems Biology
Vector quantization
High-dimensional data
Fuzzy K-means
Alternating optimization algorithm
Reconstructed information
ISSN:1868-8071, 1868-808X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Clustering techniques play a pivotal role in unveiling the inherent structure of unlabeled data. When dealing with overlapping clusters, traditional hard clustering methods encounter challenges. As a representative of soft clustering methods, Fuzzy K-Means (FKM) enables data points to be assigned different degrees of membership to multiple clusters, offering a solution to this problem. However, when dealing with high-dimensional data, the performance of FKM is often affected by redundant features and noise. To address this limitation, this paper introduces a Fuzzy K-Means Clustering with Reconstructed Information (FKMRI) method. This method combines the reconstruction term with a cluster weight variable to effectively capture the true nature of data structure, thereby enhancing the clustering capability of FKM in high-dimensional spaces. We theoretically analyze the convergence of the FKMRI algorithm and prove its time complexity to be O ( c + P ( c ) ) n d 2 + c n d . Finally, we evaluate the performance of FKMRI on standard benchmark datasets including Yale-32x32, Yale-64x64, ORL-32x32, and ORL-64x64. The results demonstrate that, in comparison to five current state-of-the-art algorithms (K-Means, FKM, Kernel-km, RSFKM, DFKM), FKMRI exhibits an average improvement of over 18% in terms of accuracy rate (ACC) and normalized mutual information (NMI). These findings convincingly validate the effectiveness and efficiency of the proposed algorithm in handling high-dimensional data clustering, providing valuable support for related research fields.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-024-02167-7