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Entropy in Fuzzy k-Means Algorithm for Multi-view Data

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Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23) (ACR 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 700))

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Abstract

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.

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Acknowledgement

This work was supported by the Internal Grant of Sultan Qaboos University (Grant No. IG/SCI/COMP/21/01).

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Authors and Affiliations

  1. Department of Computer Science, College of science, Sultan Qaboos University, Muscat, Oman

    Imran Khan, Maya ALghafri & Abdelhamid Abdessalem

Authors
  1. Imran Khan
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  2. Maya ALghafri
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  3. Abdelhamid Abdessalem
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Corresponding author

Correspondence to Imran Khan .

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Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Charles Sturt University, Sydney, NSW, Australia

    Abeer Al Sadoon

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Khan, I., ALghafri, M., Abdessalem, A. (2023). Entropy in Fuzzy k-Means Algorithm for Multi-view Data. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23). ACR 2023. Lecture Notes in Networks and Systems, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-031-33743-7_10

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