# Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data Clustering

## TL;DR

This paper introduces a memory-enriched Big Bang Big Crunch algorithm tailored for data clustering, combining heuristic and memory-based strategies to improve clustering performance over traditional methods.

## Contribution

A novel hybrid clustering algorithm that integrates memory schemes with the Big Bang Big Crunch heuristic, enhancing clustering accuracy and efficiency.

## Key findings

- Outperforms similar heuristic clustering algorithms on benchmark datasets.
- Demonstrates significant improvement over traditional methods like k-means.
- Effective in diverse clustering scenarios with complex data structures.

## Abstract

Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang-big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory based scheme as compared to its similar heuristic techniques. Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets. The experimental results show the significant superiority of the proposed method over the similar algorithms.