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.

本文回顾了最新的数据聚类方法，包括传统算法 K-means 的局限性以及 Swarm-based 算法如何解决大数据集聚类的问题，并比较它们哪个适合特定的实际问题。