  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.
