Comparison of Distributed K-Means and Distributed Fuzzy C-Means Algorithms for Text Clustering

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I Made Artha Agastya
Teguh Bharata Adji
Noor Akhmad Setiawan


Text clustering has been developed in distributed system due to increasing data. The popular algorithms like K-Means (KM) and Fuzzy C-Means (FCM) are combined with MapReduce algorithm in Hadoop Environment to be distributable and parallelizable. The problem is performance comparison between Distributed KM (DKM) and Distributed FCM (DFCM) that use Tanimoto Distance Measure (TDM) has not been studied yet. It is important because TDM’s characteristics are scale invariant while allowing discrimination collinear vectors. This work compared the combination of TDM with DKM (DKM-T) and TDM with DFCM (DFCM-T) to acquire performance of both algorithms. The result shows that DFCM-T has better intra-cluster and inter-cluster densities than those of DKM-T. Moreover, DFCM-T has lower processing time than that of DKM-T when total nodes used are 4 and 8. DFCM-T and DKM-T could perform clustering of 1,400,000 text files in 16.18 and 9.74 minutes but the preprocessing times take hours.


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How to Cite
Agastya, I. M. A., Adji, T. B., & Setiawan, N. A. (2017). Comparison of Distributed K-Means and Distributed Fuzzy C-Means Algorithms for Text Clustering. Communications in Science and Technology, 2(1).


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