Abstract
In this paper, a Gödel number-based encoding technique is proposed to encode each object of a dataset before applying any clustering algorithm. This encoding technique converts the objects into a decimal string while maintaining the properties of the features. The results of all standard existing clustering algorithms after applying this encoding are evaluated based on benchmark metrics like, Silhouette Score, Davis Bouldin, Calinski Harabasz and Dunn Index. In comparison to the existing clustering algorithms if one uses Gödel number-based encoding over the dataset, it gives better performance.
This work is partially supported by Start-up Research Grant (File number: SRG/2022/002098), SERB, Govt. of India.
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The authors are grateful to Prof. Sukanta Das for his valuable comments.
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Narodia Parth, P., Bhattacharjee, K. (2023). Gödel Number Based Encoding Technique for Effective Clustering. In: Maji, P., Huang, T., Pal, N.R., Chaudhury, S., De, R.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2023. Lecture Notes in Computer Science, vol 14301. Springer, Cham. https://doi.org/10.1007/978-3-031-45170-6_6
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