Data mining focuses on discovering interesting, non-trivial and meaningful information from large datasets. Data clustering is one of the unsupervised and descriptive data mining task which group data based on similarity features and physically stored together. As a partitioning clustering method, K-means is widely used due to its simplicity and easiness of implementation. But this method has limitations such as local optimal convergence and initial point sensibility. Due to these impediments, nature inspired Swarm based algorithms such as Artificial Bee Colony Algorithm, Ant Colony Optimization, Firefly Algorithm, Bat Algorithm and etc. are used for data clustering to cope with larger datasets with lack and inconsistency of data. In some cases, those algorithms are used with traditional approaches such as K-means as hybrid approaches to produce better results. This paper reviews the performances of these new approaches and compares which is best for certain problematic situation.

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