TY - GEN
T1 - Entropy in Fuzzy k-Means Algorithm for Multi-view Data
AU - Khan, Imran
AU - ALghafri, Maya
AU - Abdessalem, Abdelhamid
N1 - Funding Information:
This work was supported by the Internal Grant of Sultan Qaboos University (Grant No. IG/SCI/COMP/21/01).
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Multi-view data clustering plays a crucial role in various real-world applications. This kind of data from various domains can exhibit a range of distributions, making it challenging for algorithms to uncover robust patterns. This paper extends the fuzzy k-means clustering algorithm to cluster multi-view data. The objective function includes two additional matrixes to measure the compactness of each view and the importance of individual features. The objective function also includes entropy weights. Experiments on real-life data indicate that the proposed algorithm outperforms current state-of-the-art algorithms. These set of algorithms comprises of clustering techniques that incorporate variable weighting, such as W-k-means [11], LAC [9], and EWKM [13], along with a multiview clustering algorithm called TW-k-means [6]. The evaluation of the algorithms involves measuring their accuracy, as well as comparing their respective running times. A comprehensive discussion on the proposed algorithm’s properties was conducted, where all its parameters were fine-tuned and analyzed in detail.
AB - Multi-view data clustering plays a crucial role in various real-world applications. This kind of data from various domains can exhibit a range of distributions, making it challenging for algorithms to uncover robust patterns. This paper extends the fuzzy k-means clustering algorithm to cluster multi-view data. The objective function includes two additional matrixes to measure the compactness of each view and the importance of individual features. The objective function also includes entropy weights. Experiments on real-life data indicate that the proposed algorithm outperforms current state-of-the-art algorithms. These set of algorithms comprises of clustering techniques that incorporate variable weighting, such as W-k-means [11], LAC [9], and EWKM [13], along with a multiview clustering algorithm called TW-k-means [6]. The evaluation of the algorithms involves measuring their accuracy, as well as comparing their respective running times. A comprehensive discussion on the proposed algorithm’s properties was conducted, where all its parameters were fine-tuned and analyzed in detail.
KW - Multi-view
KW - clustering
KW - entropy
KW - k-means
KW - variable weights
KW - view weights
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UR - https://www.mendeley.com/catalogue/3bebcb13-f9a4-3037-9fce-2bb9f4870cb9/
U2 - 10.1007/978-3-031-33743-7_10
DO - 10.1007/978-3-031-33743-7_10
M3 - Conference contribution
AN - SCOPUS:85163347286
SN - 9783031337420
T3 - Lecture Notes in Networks and Systems
SP - 120
EP - 133
BT - Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23)
A2 - Daimi, Kevin
A2 - Al Sadoon, Abeer
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Advances in Computing Research, ACR’23
Y2 - 8 May 2023 through 10 May 2023
ER -