Comparing the Efficiency of Two Clustering Techniques

Clustering is the method of analyzing and organizing data such that data which share similar characteristics are grouped together. Clustering is used in various fields including Data Mining [19], [20], [21] and Machine Learning [28], [29], [30]. Much research has been done in the past to efficiently cluster data. Various algorithms and methods have been devised for the same. Hierarchical clustering [22], [23], partitional clustering [24], nearest neighbor clustering [26], [27], fuzzy clustering [25] are some popular techniques used for clustering [6]. This paper conducts a survey of two different methods of clustering. While both the algorithms are basically hierarchical in nature, the difference comes in their implementation. The first algorithm incorporates techniques from association rule problems [16]. On the other hand, the second one incorporates techniques from partitional clustering methods [31], [32]. This paper provides a summary of the implementation of both the algorithms and does a comparison of their behavior.