Weighted Multiview K-Means Clustering with L2 Regularization

In the era of big data, cloud, internet of things, virtual communities, and interconnected networks, the prominence of multiview data is undeniable. This type of data encapsulates diverse feature components across varying perspectives, each offering unique insights into the same underlying samples....

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Bibliographic Details
Published in:Symmetry (Basel) 2024-12, Vol.16 (12), p.1646
Main Authors: Hussain, Ishtiaq, Nataliani, Yessica, Mehboob, Ali, Hussain, Atif, Mujlid, Hana M, Almaliki, Faris A, Rahimi, Nouf M
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Cluster analysis
Clustering
Collaboration
Datasets
Dermatology
Euclidean space
Feature selection
Image retrieval
Internet of Things
k-means
Literature reviews
Machine learning
multiview
Regularization
Sparsity
Vector quantization
Virtual networks
weighted multiview k-means (W-MV-KM)
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Summary:In the era of big data, cloud, internet of things, virtual communities, and interconnected networks, the prominence of multiview data is undeniable. This type of data encapsulates diverse feature components across varying perspectives, each offering unique insights into the same underlying samples. Despite being sourced from diverse settings and domains, these data serve the common purpose of describing the same samples, establishing a significant interrelation among them. Thus, there arises a necessity for the development of multiview clustering methodologies capable of leveraging the wealth of information available across multiple views. This study introduces two novel weighted multiview k-means algorithms, W-MV-KM and weighted multiview k-means using L2 regularization, W-MV-KM-L2, designed specifically for clustering multiview data. These algorithms incorporate feature weights and view weights within the k-means (KM) framework. Our approach emphasizes a weighted multiview learning strategy, which assigns varying degrees of importance to individual views. We evaluate the clustering performance of our algorithms on seven diverse benchmark datasets spanning dermatology, textual, image, and digit domains. Through extensive experimentation and comparisons with existing methods, we showcase the superior effectiveness and utility of our newly introduced W-MV-KM-L2 algorithm.
ISSN:2073-8994
DOI:10.3390/sym16121646