Abstract
The paper reports an efficient clustering technique for high-dimensional data using cycles of reversible finite cellular automata (CAs). As any arbitrary cellular automaton (CA) is not useful for maintaining less intra-cluster and high inter-cluster distance essential for an effective clustering, first the candidate CA rules have been identified based on theoretical properties of information flow and self-replication. Three stages of hierarchical clustering are incorporated over the encoded real dataset with any number of features. Because of the inherent parallelism of our algorithm, its running time is polynomial to the number of objects in the dataset which avoids the limitations of the existing CA-based clustering techniques. With respect to various standard benchmark performance metrics, our algorithm is at par with the other existing algorithms.
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Acknowledgements
This work is partially supported by Start-up Research Grant (File number SRG/2022/002098), SERB, Department of Science & Technology, Government of India. The authors are grateful to Prof. Sukanta Das and Dr. Sukanya Mukherjee for their insightful comments and discussions which have been useful for this work. A special thanks goes to Mr. Subrata Paul for his help in coding.
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Abhishek, S., Dharwish, M., Das, A., Bhattacharjee, K. (2023). A Cellular Automata-Based Clustering Technique for High-Dimensional Data. In: Das, S., Martinez, G.J. (eds) Proceedings of Second Asian Symposium on Cellular Automata Technology. ASCAT 2023. Advances in Intelligent Systems and Computing, vol 1443. Springer, Singapore. https://doi.org/10.1007/978-981-99-0688-8_4
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DOI: https://doi.org/10.1007/978-981-99-0688-8_4
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