Gödel Number based Clustering Algorithm with Decimal First Degree Cellular Automata
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916239163523072 |
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| author | Vikrant, Vicky P, Narodia Parth Bhattacharjee, Kamalika |
| author_facet | Vikrant, Vicky P, Narodia Parth Bhattacharjee, Kamalika |
| contents | In this paper, a decimal first degree cellular automata (FDCA) based clustering algorithm is proposed where clusters are created based on reachability. Cyclic spaces are created and configurations which are in the same cycle are treated as the same cluster. Here, real-life data objects are encoded into decimal strings using Gödel number based encoding. The benefits of the scheme is, it reduces the encoded string length while maintaining the features properties. Candidate CA rules are identified based on some theoretical criteria such as self-replication and information flow. An iterative algorithm is developed to generate the desired number of clusters over three stages. The results of the clustering are evaluated based on benchmark clustering metrics such as Silhouette score, Davis Bouldin, Calinski Harabasz and Dunn Index. In comparison with the existing state-of-the-art clustering algorithms, our proposed algorithm gives better performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04881 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gödel Number based Clustering Algorithm with Decimal First Degree Cellular Automata Vikrant, Vicky P, Narodia Parth Bhattacharjee, Kamalika Formal Languages and Automata Theory Emerging Technologies Machine Learning In this paper, a decimal first degree cellular automata (FDCA) based clustering algorithm is proposed where clusters are created based on reachability. Cyclic spaces are created and configurations which are in the same cycle are treated as the same cluster. Here, real-life data objects are encoded into decimal strings using Gödel number based encoding. The benefits of the scheme is, it reduces the encoded string length while maintaining the features properties. Candidate CA rules are identified based on some theoretical criteria such as self-replication and information flow. An iterative algorithm is developed to generate the desired number of clusters over three stages. The results of the clustering are evaluated based on benchmark clustering metrics such as Silhouette score, Davis Bouldin, Calinski Harabasz and Dunn Index. In comparison with the existing state-of-the-art clustering algorithms, our proposed algorithm gives better performance. |
| title | Gödel Number based Clustering Algorithm with Decimal First Degree Cellular Automata |
| topic | Formal Languages and Automata Theory Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2405.04881 |