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

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.


K-Means; FCM; Tanimoto Distance; MapReduce; Hadoop

Full Text:



X. Wu, X. Zhu, G. Wu, and W. Ding, “Data mining with big data,” Knowl. Data …, vol. 26, no. 1, pp. 97–107, 2014.

A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int. J. Inf. Manage., vol. 35, no. 2, pp. 137–144, 2015.

T. White, Hadoop : The Definitive Guide. 2015.

S. Sathya and N. Rajendran, “A Review on Text Mining Techniques,” Int. J. Comput. Sci. Trends Technol., vol. 3, no. 5, pp. 274–284, 2013.

R. C. Esteves Rui, “Using Mahout for clustering Wikipedia’s latest articles: A comparison between k-means and fuzzy c-means in the cloud,” Proc. - 2011 3rd IEEE Int. Conf. Cloud Comput. Technol. Sci. CloudCom 2011, pp. 565–569, 2011.

S. Madhukumar and N. Santhiyakumari, “Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain,” Egypt. J. Radiol. Nucl. Med., vol. 46, no. 2, pp. 475–479, 2015.

S. K. Sahu and S. K. Jena, “A Study of K-Means and C-Means Clustering Algorithms for Intrusion Detection Product Development,” Int. J. Innov. Manag. Technol., vol. 5, no. 3, pp. 207–213, 2014.

S. Panda, S. Sahu, P. Jena, and S. Chattopadhyay, “Comparing fuzzy-C means and K-means clustering techniques: A comprehensive study,” Adv. Intell. Soft Comput., vol. 166 AISC, no. VOL. 1, pp. 451–460, 2012.

L. Sahu and B. R. Mohan, “An improved K-means algorithm using modified cosine distance measure for document clustering using Mahout with Hadoop,” 9th Int. Conf. Ind. Inf. Syst. ICIIS 2014, 2015.

E. Jain and S. K. Jain, “Using Mahout for clustering similar Twitter users: Performance evaluation of k-means and its comparison with fuzzy k-means,” Proc. - 5th IEEE Int. Conf. Comput. Commun. Technol. ICCCT 2014, pp. 29–33, 2015.

E. Jain and S. K. Jain, “Categorizing twitter users on the basis of their interests using hadoop/mahout platform,” 9th Int. Conf. Ind. Inf. Syst. ICIIS 2014, 2015.

P. Muniz De Avila et al., “Comparing K-Means and Mean Shift Algorithms Performance Using Mahout in a Private Cloud Environment,” J. Commun. Comput., vol. 11, pp. 45–51, 2014.

S. Owen, R. Anil, T. Dunning, and E. Friedman, Mahout in Action. 2011.

J. Ghosh and A. Strehl, “Similarity-based text clustering: A comparative study,” Group. Multidimens. Data Recent Adv. Clust., no. ii, pp. 73–97, 2006.

A. Huang, “Similarity measures for text document clustering,” Proc. Sixth New Zeal., no. April, pp. 49–56, 2008.

A. S. Joydeep, E. Strehl, J. Ghosh, R. Mooney, and A. Strehl, “Impact of Similarity Measures on Web-page Clustering,” Work. Artif. Intell. Web Search (AAAI 2000), 2000.

A. Rangrej, “Comparative Study of Clustering Techniques for Short Text Documents,” Media, pp. 111–112, 2011.

X. Zhang, J. Zhao, and Y. LeCun, “Character-level Convolutional Networks for Text Classification,” Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, pp. 3057–3061, 2015.

M. Eroglu S., Toprak S., Urgan O, MD, Ozge E. Onur, MD, Arzu Denizbasi, MD, Haldun Akoglu, MD, Cigdem Ozpolat, MD, Ebru Akoglu, Hadoop Solutions, vol. 33. 2012.

ASF, “Apache Mahout: Scalable machine learning and data mining.” 2016.



  • There are currently no refbacks.

Copyright (c) 2017 Communications in Science and Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International Licensejoomla
visitors View My Stats