- 期刊
- OpenAccess
Performance Comparison of Clustering Techniques
並列摘要
Data mining consists to extracting or ＂mining＂ information from large quantity of data. Clustering is one of the most significant research areas in the domain of data mining. Clustering signifies making groups of objects founded on their features where the objects of the same groups are similar and those belonging in different groups are not similar. This study reviews two Clustering Algorithms of the representative clustering techniques: K-modes and K-medoids algorithms. The two algorithms are experimented and evaluated on partitioning Y-STR data. All these algorithms are compared according to the following factors: certain number times of run, precision and recall. The global results show that K-mode clustering is better than the k-medoid in clustering Y-STR data.
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