A Comparative Analysis of Clustering Algorithms

Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
Clustering is a process of grouping a set of similar data objects within the same group based on similarity criteria (i.e. based on a set of attributes). There are many clustering algorithms. This paper is to perform a comparative analysis of four clustering algorithms namely K-means algorithm, hierarchical algorithm, expectation and maximization algorithm and density based algorithm. These algorithms are compared in terms of efficiency and accuracy, using WEKA tool. The data for clustering is used in normalized and as well as unnormalized format. In terms of efficiency and accuracy K-means produces better results as compared to other algorithms.

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