Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)

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Parametric entropy based Cluster Centriod Initialization for k-means clustering of various Image datasets

Authors
Faheem Hussayn1, *, Shahid M. Shah1
1Communication Control & Learning Lab (C2L2), Department of Electronics & Communication Engineering, National Institute of Technology Srinagar, Srinagar, India
*Corresponding author. Email: faheem_2023phacse002@nitsri.ac.in
Corresponding Author
Faheem Hussayn
Available Online 4 October 2024.
DOI
10.2991/978-94-6463-529-4_5How to use a DOI?
Keywords
Entropy; Clustering; K-Means Algorithm; Unsupervised Learning
Abstract

One of the most employed yet simple algorithm for cluster analysis is the k-means algorithm. k-means has successfully witnessed its use in artificial intelligence, market segmentation, fraud detection, data mining, psychology, etc., only to name a few. The k-means algorithm, however, does not always yield the best quality results. Its performance heavily depends upon the number of clusters supplied and the proper initialization of the cluster centroids or seeds.

In this paper, we analyze the performance of k-means on image data by employing parametric entropies in an entropy-based centroid initialization method and propose the best-fitting entropy measures for general image datasets. We use several entropies like Taneja entropy, Kapur entropy, Aczel Daroczy entropy, and Sharma Mittal entropy. We observe that different entropies provide better results for different datasets than the conventional methods. We have applied our proposed algorithm on these datasets: Satellites, Toys, Fruits, Cars, Brain MRI, and COVID X-Ray.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
Series
Advances in Engineering Research
Publication Date
4 October 2024
ISBN
978-94-6463-529-4
ISSN
2352-5401
DOI
10.2991/978-94-6463-529-4_5How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Faheem Hussayn
AU  - Shahid M. Shah
PY  - 2024
DA  - 2024/10/04
TI  - Parametric entropy based Cluster Centriod Initialization for k-means clustering of various Image datasets
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 46
EP  - 57
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_5
DO  - 10.2991/978-94-6463-529-4_5
ID  - Hussayn2024
ER  -