Hierarchical Clustering using Reversible Binary Cellular Automata for High-Dimensional Data

TL;DR

This paper introduces a hierarchical clustering method for high-dimensional data using reversible cellular automata cycles, improving cluster grouping by cycle relationships and rule selection to reduce computational costs.

Contribution

It presents a novel CA-based hierarchical clustering algorithm that accounts for cycle relationships across different automata and optimizes rule selection for efficiency.

Findings

Performs comparably to existing algorithms on benchmark datasets.

Reduces computational cost through rule selection and cycle analysis.

Effective in diverse fields like healthcare and agriculture.

Abstract

This work proposes a hierarchical clustering algorithm for high-dimensional datasets using the cyclic space of reversible finite cellular automata. In cellular automaton (CA) based clustering, if two objects belong to the same cycle, they are closely related and considered as part of the same cluster. However, if a high-dimensional dataset is clustered using the cycles of one CA, closely related objects may belong to different cycles. This paper identifies the relationship between objects in two different cycles based on the median of all elements in each cycle so that they can be grouped in the next stage. Further, to minimize the number of intermediate clusters which in turn reduces the computational cost, a rule selection strategy is taken to find the best rules based on information propagation and cycle structure. After encoding the dataset using frequency-based encoding such that the consecutive data elements maintain a minimum hamming distance in encoded form, our proposed clustering algorithm iterates over three stages to finally cluster the data elements into the desired number of clusters given by user. This algorithm can be applied to various fields, including healthcare, sports, chemical research, agriculture, etc. When verified over standard benchmark datasets with various performance metrics, our algorithm is at par with the existing algorithms with quadratic time complexity.