Scholarlink

<p>In this paper, we propose a clustering algorithm based on decimal first-degree cellular automata (FDCA). Clusters are formed by <i>reachability</i> within <i>cyclic spaces</i>, where configurations in the same cycle are grouped into a single cluster. We encode data objects into compact decimal strings using Gödel number-based encoding, reducing string length while preserving key feature properties. Based on two theoretical properties like rate of self-replication and flow of information, a set of candidate rules are chosen. Then a three-stage algorithm is developed to get the desired number of clusters. Experimental results show that our method is at par with the existing algorithms in terms of clustering performance, computational efficiency, and feature preservation, offering a promising solution for clustering real-world datasets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Clustering with Gödel numbering and decimal first degree cellular automata

  • 期刊论文

  • 发表日期：

    2025年2月22日

查看全文
  • Vicky Vikrant,
  • Kamalika Bhattacharjee

摘要

In this paper, we propose a clustering algorithm based on decimal first-degree cellular automata (FDCA). Clusters are formed by reachability within cyclic spaces, where configurations in the same cycle are grouped into a single cluster. We encode data objects into compact decimal strings using Gödel number-based encoding, reducing string length while preserving key feature properties. Based on two theoretical properties like rate of self-replication and flow of information, a set of candidate rules are chosen. Then a three-stage algorithm is developed to get the desired number of clusters. Experimental results show that our method is at par with the existing algorithms in terms of clustering performance, computational efficiency, and feature preservation, offering a promising solution for clustering real-world datasets.