Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data
•The partition based clustering algorithms k-Means and Fuzzy C-Means algorithms are taken for analysis via its computational time.•The distance between servers and user connections of telecommunication data are taken for clustering.•The computational time and number of connections in each server was...
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| Published in: | Applied soft computing Vol. 19; pp. 134 - 146 |
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| Format: | Journal Article |
| Language: | English |
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01.06.2014
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | •The partition based clustering algorithms k-Means and Fuzzy C-Means algorithms are taken for analysis via its computational time.•The distance between servers and user connections of telecommunication data are taken for clustering.•The computational time and number of connections in each server was reported by the algorithms after clustering process.•The distribution of data points by k-Means algorithm is even to all the data centers, but, it is not even by the FCM algorithm.•From the experimental analysis, the computational time of k-Means algorithm is less than the FCM algorithm.
Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. Cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-Means and Fuzzy C-Means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in Fuzzy C-Means algorithm than the k-Means. |
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| AbstractList | •The partition based clustering algorithms k-Means and Fuzzy C-Means algorithms are taken for analysis via its computational time.•The distance between servers and user connections of telecommunication data are taken for clustering.•The computational time and number of connections in each server was reported by the algorithms after clustering process.•The distribution of data points by k-Means algorithm is even to all the data centers, but, it is not even by the FCM algorithm.•From the experimental analysis, the computational time of k-Means algorithm is less than the FCM algorithm.
Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. Cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-Means and Fuzzy C-Means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in Fuzzy C-Means algorithm than the k-Means. |
| Author | T., Velmurugan |
| Author_xml | – sequence: 1 givenname: Velmurugan orcidid: 0000-0001-8857-0136 surname: T. fullname: T., Velmurugan email: velmurugan_dgvc@yahoo.co.in organization: PG and Research Department of Computer Science, D.G. Vaishnav College, Chennai - 600106, India |
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| References | Chen (bib0125) 2006; 37 Vijayakumar, Parvathi (bib0085) 2010; 39 Han, Kamber (bib0040) 2006 Susana, Leiva-Valdebenito, Francisco, Torres-Aviles (bib0070) 2010; 33 Velmurugan, Santhanam (bib0145) 2011; 27 Bezdek, Ehrlich, Full (bib0020) 1984; 10 Shanmugam, Suryanarayana, Chandrashekar, Manjunath (bib0065) 2011; 7 Lin, Chen (bib0130) 2010; 27 Al-Zoubi, Hudaib, Al-Shboul (bib0010) 2007 Napoleon, Ganga Lakshmi (bib0075) 2010; 2 Lin, Hsu (bib0105) 2011; 11 Berkhin (bib0015) 2002 Hsiao, Hwang, Chen, Tsai (bib0140) 2005; 13 Jain, Dubes (bib0050) 1988 Chen (bib0135) 2010; 37 Jain, Murty, Flynn (bib0055) 1999; 31 Shen, Cheng, Chen, Tsai, Cheng (bib0110) 2011; 7 Chen (bib0120) 2011; 17 Yong, Chongxun, Pan (bib0100) 2004; 4 Kuo, Chen (bib0115) 2011; 7 Park, Lee, Jun (bib0080) 2009 Bezdek (bib0025) 1981 Kaufman, Rousseeuw (bib0060) 2009; vol. 344 Velmurugan, Santhanam (bib0095) 2010; 46 Velmurugan, Santhanam (bib0090) 2010; 6 Rakhlin, Caponnetto (bib0005) 2007; vol. 12 Bradley, Fayyad, Reina (bib0030) 1998 Hartigan (bib0045) 1975, January Benderskaya, Zhukova (bib0035) 2011; 91 Shanmugam (10.1016/j.asoc.2014.02.011_bib0065) 2011; 7 Yong (10.1016/j.asoc.2014.02.011_bib0100) 2004; 4 Lin (10.1016/j.asoc.2014.02.011_bib0130) 2010; 27 Chen (10.1016/j.asoc.2014.02.011_bib0125) 2006; 37 Jain (10.1016/j.asoc.2014.02.011_bib0055) 1999; 31 Hartigan (10.1016/j.asoc.2014.02.011_bib0045) 1975 Velmurugan (10.1016/j.asoc.2014.02.011_bib0095) 2010; 46 Lin (10.1016/j.asoc.2014.02.011_bib0105) 2011; 11 Kaufman (10.1016/j.asoc.2014.02.011_bib0060) 2009; vol. 344 Jain (10.1016/j.asoc.2014.02.011_bib0050) 1988 Napoleon (10.1016/j.asoc.2014.02.011_bib0075) 2010; 2 Chen (10.1016/j.asoc.2014.02.011_bib0120) 2011; 17 Rakhlin (10.1016/j.asoc.2014.02.011_bib0005) 2007; vol. 12 Kuo (10.1016/j.asoc.2014.02.011_bib0115) 2011; 7 Park (10.1016/j.asoc.2014.02.011_bib0080) 2009 Velmurugan (10.1016/j.asoc.2014.02.011_bib0145) 2011; 27 Hsiao (10.1016/j.asoc.2014.02.011_bib0140) 2005; 13 Chen (10.1016/j.asoc.2014.02.011_bib0135) 2010; 37 Berkhin (10.1016/j.asoc.2014.02.011_bib0015) 2002 Bradley (10.1016/j.asoc.2014.02.011_bib0030) 1998 Bezdek (10.1016/j.asoc.2014.02.011_bib0025) 1981 Shen (10.1016/j.asoc.2014.02.011_bib0110) 2011; 7 Susana (10.1016/j.asoc.2014.02.011_bib0070) 2010; 33 Bezdek (10.1016/j.asoc.2014.02.011_bib0020) 1984; 10 Han (10.1016/j.asoc.2014.02.011_bib0040) 2006 Al-Zoubi (10.1016/j.asoc.2014.02.011_bib0010) 2007 Vijayakumar (10.1016/j.asoc.2014.02.011_bib0085) 2010; 39 Benderskaya (10.1016/j.asoc.2014.02.011_bib0035) 2011; 91 Velmurugan (10.1016/j.asoc.2014.02.011_bib0090) 2010; 6 |
| References_xml | – year: 1988 ident: bib0050 article-title: Algorithms for Clustering Data – year: 1981 ident: bib0025 article-title: Pattern Recognition with Fuzzy Objective Function Algorithms – year: 2002 ident: bib0015 article-title: Survey of Clustering Data Mining Techniques, Technical Report – volume: 27 start-page: 19 year: 2011 end-page: 30 ident: bib0145 article-title: A comparative analysis between k-medoids and fuzzy c-means clustering algorithms for statistically distributed data points publication-title: Journal of Theoretical and Applied Information Technology – volume: 4 year: 2004 ident: bib0100 article-title: A novel fuzzy c-means clustering algorithm for image thresholding publication-title: Measurement Science Review – volume: 39 start-page: 234 year: 2010 end-page: 242 ident: bib0085 article-title: Concept mining of high volume data streams in network traffic using hierarchical clustering publication-title: European Journal of Scientific Research – start-page: 9 year: 1998 end-page: 15 ident: bib0030 article-title: Scaling clustering algorithms to large databases publication-title: Proceedings of the 4th International Conference on Knowledge Discovery & Data Mining (KDD98) – volume: 6 start-page: 363 year: 2010 end-page: 368 ident: bib0090 article-title: Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points publication-title: Journal of Computer Science – year: 2006 ident: bib0040 article-title: Data Mining: Concepts and Techniques – volume: 7 start-page: 657 year: 2011 end-page: 663 ident: bib0065 article-title: A novel approach to medical image segmentation publication-title: Journal of Computer Science – volume: 13 start-page: 152 year: 2005 end-page: 163 ident: bib0140 article-title: Robust stabilization of nonlinear multiple time-delay large-scale systems via decentralized fuzzy control publication-title: IEEE Transactions on Fuzzy Systems – volume: 17 start-page: 1693 year: 2011 end-page: 1702 ident: bib0120 article-title: Fuzzy control of interconnected structural systems using the fuzzy Lyapunov method publication-title: Journal of Vibration and Control – volume: 7 start-page: 253 year: 2011 end-page: 268 ident: bib0115 article-title: Application of quality function deployment to improve the quality of Internet shopping website interface design publication-title: International Journal of Innovative Computing, Information and Control – volume: 37 start-page: 993 year: 2010 end-page: 997 ident: bib0135 article-title: Application of data mining to the spatial heterogeneity of foreclosed mortgages publication-title: Expert Systems with Applications – volume: vol. 344 year: 2009 ident: bib0060 publication-title: Finding groups in data: an introduction to cluster analysis – volume: 7 start-page: 805 year: 2011 end-page: 816 ident: bib0110 article-title: A fuzzy AHP-based fault diagnosis for semiconductor lithography process publication-title: International Journal of Innovative Computing, Information and Control – volume: 91 start-page: 423 year: 2011 end-page: 431 ident: bib0035 article-title: Self-organized clustering and classification: a unified approach via distributed chaotic computing, international symposium on distributed computing and artificial intelligence publication-title: Advances in Intelligent and Soft Computing – volume: 2 start-page: 2409 year: 2010 end-page: 2413 ident: bib0075 article-title: An enhanced k-means algorithm to improve the efficiency using normal distribution data points publication-title: International Journal on Computer Science and Engineering – volume: 11 start-page: 561 year: 2011 end-page: 573 ident: bib0105 article-title: Designing a model of FANP in brand image decision making publication-title: Applied Soft Computing – volume: 31 year: 1999 ident: bib0055 article-title: Data clustering: a review publication-title: ACM Computing Surveys – volume: 10 start-page: 191 year: 1984 end-page: 203 ident: bib0020 article-title: FCM: the Fuzzy C-Means clustering algorithm publication-title: Computers and Geosciences – year: 1975, January ident: bib0045 article-title: Clustering Algorithms – volume: 37 start-page: 624 year: 2006 end-page: 629 ident: bib0125 article-title: Stability conditions of fuzzy systems and its application to structural and mechanical systems publication-title: Advances in Engineering Software – volume: 33 start-page: 321 year: 2010 end-page: 339 ident: bib0070 article-title: A review of the most common partition algorithms in cluster analysis: a comparative study publication-title: Colombian Journal of Statistics – volume: 46 start-page: 320 year: 2010 end-page: 330 ident: bib0095 article-title: Performance evaluation of k-means and fuzzy C-means clustering algorithms for statistical distributions of input data points publication-title: European Journal of Scientific Research – year: 2009 ident: bib0080 article-title: A k-Means-Like Algorithm for k-Medoids Clustering and Its Performance – volume: vol. 12 start-page: 216 year: 2007 end-page: 222 ident: bib0005 article-title: Stability of k-Means clustering publication-title: Advances in Neural Information Processing Systems – start-page: 28 year: 2007 end-page: 32 ident: bib0010 article-title: A fast fuzzy clustering algorithm publication-title: Proceedings of the 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases – volume: 27 start-page: 5 year: 2010 end-page: 19 ident: bib0130 article-title: Application of fuzzy models for the monitoring of ecologically sensitive ecosystems in a dynamic semi-arid landscape from satellite imagery publication-title: Engineering Computations – year: 2002 ident: 10.1016/j.asoc.2014.02.011_bib0015 – volume: vol. 344 year: 2009 ident: 10.1016/j.asoc.2014.02.011_bib0060 – volume: 91 start-page: 423 year: 2011 ident: 10.1016/j.asoc.2014.02.011_bib0035 article-title: Self-organized clustering and classification: a unified approach via distributed chaotic computing, international symposium on distributed computing and artificial intelligence publication-title: Advances in Intelligent and Soft Computing doi: 10.1007/978-3-642-19934-9_54 – volume: 27 start-page: 5 issue: 1 year: 2010 ident: 10.1016/j.asoc.2014.02.011_bib0130 article-title: Application of fuzzy models for the monitoring of ecologically sensitive ecosystems in a dynamic semi-arid landscape from satellite imagery publication-title: Engineering Computations doi: 10.1108/02644401011008504 – volume: 7 start-page: 805 issue: 2 year: 2011 ident: 10.1016/j.asoc.2014.02.011_bib0110 article-title: A fuzzy AHP-based fault diagnosis for semiconductor lithography process publication-title: International Journal of Innovative Computing, Information and Control – volume: 11 start-page: 561 issue: 1 year: 2011 ident: 10.1016/j.asoc.2014.02.011_bib0105 article-title: Designing a model of FANP in brand image decision making publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2009.12.015 – volume: 7 start-page: 657 issue: 5 year: 2011 ident: 10.1016/j.asoc.2014.02.011_bib0065 article-title: A novel approach to medical image segmentation publication-title: Journal of Computer Science doi: 10.3844/jcssp.2011.657.663 – volume: 46 start-page: 320 issue: 3 year: 2010 ident: 10.1016/j.asoc.2014.02.011_bib0095 article-title: Performance evaluation of k-means and fuzzy C-means clustering algorithms for statistical distributions of input data points publication-title: European Journal of Scientific Research – volume: 37 start-page: 993 issue: 2 year: 2010 ident: 10.1016/j.asoc.2014.02.011_bib0135 article-title: Application of data mining to the spatial heterogeneity of foreclosed mortgages publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2009.05.076 – year: 2009 ident: 10.1016/j.asoc.2014.02.011_bib0080 – volume: vol. 12 start-page: 216 year: 2007 ident: 10.1016/j.asoc.2014.02.011_bib0005 article-title: Stability of k-Means clustering – volume: 7 start-page: 253 issue: 1 year: 2011 ident: 10.1016/j.asoc.2014.02.011_bib0115 article-title: Application of quality function deployment to improve the quality of Internet shopping website interface design publication-title: International Journal of Innovative Computing, Information and Control – volume: 27 start-page: 19 issue: 1 year: 2011 ident: 10.1016/j.asoc.2014.02.011_bib0145 article-title: A comparative analysis between k-medoids and fuzzy c-means clustering algorithms for statistically distributed data points publication-title: Journal of Theoretical and Applied Information Technology – volume: 39 start-page: 234 issue: 2 year: 2010 ident: 10.1016/j.asoc.2014.02.011_bib0085 article-title: Concept mining of high volume data streams in network traffic using hierarchical clustering publication-title: European Journal of Scientific Research – volume: 17 start-page: 1693 issue: 11 year: 2011 ident: 10.1016/j.asoc.2014.02.011_bib0120 article-title: Fuzzy control of interconnected structural systems using the fuzzy Lyapunov method publication-title: Journal of Vibration and Control doi: 10.1177/1077546310379625 – volume: 13 start-page: 152 year: 2005 ident: 10.1016/j.asoc.2014.02.011_bib0140 article-title: Robust stabilization of nonlinear multiple time-delay large-scale systems via decentralized fuzzy control publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2004.836067 – volume: 33 start-page: 321 issue: December (2) year: 2010 ident: 10.1016/j.asoc.2014.02.011_bib0070 article-title: A review of the most common partition algorithms in cluster analysis: a comparative study publication-title: Colombian Journal of Statistics – year: 1981 ident: 10.1016/j.asoc.2014.02.011_bib0025 – volume: 37 start-page: 624 issue: 9 year: 2006 ident: 10.1016/j.asoc.2014.02.011_bib0125 article-title: Stability conditions of fuzzy systems and its application to structural and mechanical systems publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2005.12.002 – volume: 2 start-page: 2409 issue: 7 year: 2010 ident: 10.1016/j.asoc.2014.02.011_bib0075 article-title: An enhanced k-means algorithm to improve the efficiency using normal distribution data points publication-title: International Journal on Computer Science and Engineering – year: 1988 ident: 10.1016/j.asoc.2014.02.011_bib0050 – volume: 6 start-page: 363 issue: 3 year: 2010 ident: 10.1016/j.asoc.2014.02.011_bib0090 article-title: Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points publication-title: Journal of Computer Science doi: 10.3844/jcssp.2010.363.368 – year: 1975 ident: 10.1016/j.asoc.2014.02.011_bib0045 – volume: 4 issue: 91 year: 2004 ident: 10.1016/j.asoc.2014.02.011_bib0100 article-title: A novel fuzzy c-means clustering algorithm for image thresholding publication-title: Measurement Science Review – year: 2006 ident: 10.1016/j.asoc.2014.02.011_bib0040 – start-page: 28 year: 2007 ident: 10.1016/j.asoc.2014.02.011_bib0010 article-title: A fast fuzzy clustering algorithm – volume: 31 issue: September (3) year: 1999 ident: 10.1016/j.asoc.2014.02.011_bib0055 article-title: Data clustering: a review publication-title: ACM Computing Surveys – volume: 10 start-page: 191 year: 1984 ident: 10.1016/j.asoc.2014.02.011_bib0020 article-title: FCM: the Fuzzy C-Means clustering algorithm publication-title: Computers and Geosciences doi: 10.1016/0098-3004(84)90020-7 – start-page: 9 year: 1998 ident: 10.1016/j.asoc.2014.02.011_bib0030 article-title: Scaling clustering algorithms to large databases |
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| SubjectTerms | Data analysis Data clustering Fuzzy C-Means algorithm k-Means algorithm Telecommunication data |
| Title | Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data |
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