Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data Clustering
This is an incremental improvement for clustering applications in knowledge-based systems.
The paper tackles data clustering by proposing a memory-enriched Big Bang Big Crunch optimization algorithm, which shows significant superiority over similar algorithms in experiments on benchmark datasets.
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.