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A Short Review on Different Clustering Techniques and Their Applications

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Emerging Technology in Modelling and Graphics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 937))

Abstract

In modern world, we have to deal with huge volumes of data which include image, video, text and web documents, DNA, microarray gene data, etc. Organizing such data into rational groups is a critical first step to draw inferences. Data clustering analysis has emerged as an effective technique to accurately accomplish the task of categorizing data into sensible groups. Clustering has a rich association with researches in various scientific domains. One of the most popular clustering algorithms, k-means algorithm was proposed as early as 1957. Since then, many clustering algorithms have been developed and used, to group data in various commercial and non-commercial sectors alike. In this paper, we have given concise description of the existing types of clustering approaches followed by a survey of the fields where clustering analytics has been effectively employed in pattern recognition and knowledge discovery.

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References

  1. T. Hale, How much data does the world generate every minute? (2018, June 18). Retrieved July 5, 2018, from http://www.iflscience.com/technology/how-much-data-does-the-world-generate-every-minute/

  2. A.K. Jain, Data clustering: 50 years beyond K-means. ECML/PKDD (2008)

    Google Scholar 

  3. S. Kaushik, An introduction to clustering & different methods of clustering (2016, December 10). Retrieved July 5, 2018, from https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/

  4. A. Fahad, N. Alshatri, Z. Tari, A. Alamri, I. Khalil, A.Y. Zomaya, S. Foufou, A. Bouras, A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans. Emerg. Top. Comput. 2, 267–279 (2014)

    Article  Google Scholar 

  5. G. Seif, The 5 clustering algorithms data scientists need to know (2018, February 5). Retrieved July 5, 2018, from https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68

  6. Data Clustering: Theory, Algorithms, and Applications. (n.d.). Retrieved July 12, 2018, from https://epubs.siam.org/doi/abs/10.1137/1.9780898718348.ch12

  7. F. Chamroukhi, Robust EM algorithm for model-based curve clustering, in The 2013 International Joint Conference on Neural Networks (IJCNN) (2013), pp. 1–8

    Google Scholar 

  8. Y. Yang, B. Lian, C. Chen, P. Li, DBSCAN clustering algorithm applied to identify suspicious financial transactions, in 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (2014), pp. 60–65

    Google Scholar 

  9. S.S. Alkhasov, A.N. Tselykh, A.A. Tselykh, Application of cluster analysis for the assessment of the share of fraud victims among bank card holders, in SIN (2015)

    Google Scholar 

  10. N.R. Kisore, C.B. Koteswaraiah, Improving ATM coverage area using density based clustering algorithm and voronoi diagrams. Inf. Sci. 376, 1–20 (2017)

    Article  Google Scholar 

  11. W. Bi, M. Cai, M. Liu, G. Li, A big data clustering algorithm for mitigating the risk of customer churn. IEEE Trans. Ind. Inform. 12, 1270–1281 (2016)

    Article  Google Scholar 

  12. V. Behbood, J. Lu, G. Zhang, Fuzzy refinement domain adaptation for long term prediction in banking ecosystem. IEEE Trans. Ind. Inform. 10, 1637–1646 (2014)

    Article  Google Scholar 

  13. C.P. Ezenkwu, S. Ozuomba, C. Kalu, Application of K-Means algorithm for efficient customer segmentation: a strategy for targeted customer services (2015)

    Google Scholar 

  14. P. D’Urso, L.D. Giovanni, M. Disegna, R. Massari, Bagged Clustering and its application to tourism market segmentation. Expert Syst. Appl. 40, 4944–4956 (2013)

    Article  Google Scholar 

  15. B. Aubaidan, M. Mohd, M. Albared, Comparative study of k-means and k-means++ clustering algorithms on crime domain. JCS 10, 1197–1206 (2014)

    Google Scholar 

  16. C. Yang, N. Benjamasutin, Y. Chen-Burger, Mining hidden concepts: using short text clustering and wikipedia knowledge, in 2014 28th International Conference on Advanced Information Networking and Applications Workshops (2014), pp. 675–680

    Google Scholar 

  17. R.E. Thomas, S.S. Khan, Improved clustering technique using metadata for text mining, in 2016 International Conference on Communication and Electronics Systems (ICCES) (2016), pp. 1–5

    Google Scholar 

  18. E.A. Calvillo, A. Padilla, J.M. Arteaga, J.C. Gallegos, J.T. Fernndez-Breis, Searching research papers using clustering and text mining, in CONIELECOMP 2013, 23rd International Conference on Electronics, Communications and Computing (2013), pp. 78–81

    Google Scholar 

  19. G. Kazeminouri, A. Harounabadi, S.J. Mirabedini, E. Kazemi, Web personalization with web usage mining techniques and association rules (2015)

    Google Scholar 

  20. O.J. Oyelade, O.O. Oladipupo, I.C. Obagbuwa, Application of k Means clustering algorithm for prediction of students academic performance (2010). CoRR, abs/1002.2425

  21. S. Rana, R. Garg, Application of hierarchical clustering algorithm to evaluate students performance of an institute, in 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT) (2016)

    Google Scholar 

  22. A. Waheed, M.U. Akram, S. Khalid, Z. Waheed, M.A. Khan, A. Shaukat, Hybrid features and mediods classification based robust segmentation of blood vessels. J. Med. Syst. 39, 1–14 (2015)

    Article  Google Scholar 

  23. M.U. Akram, S. Khalid, A. Tariq, M.Y. Javed, Detection of neovascularization in retinal images using multivariate m-Mediods based classifier. Comput. Med. Imaging Graph. Official J. Comput. Med. Imaging Soc. 37(5–6), 346–57 (2013)

    Article  Google Scholar 

  24. P.M. Patel, B.N. Shah, V. Shah, Image segmentation using K-mean clustering for finding tumor in medical application (2013)

    Google Scholar 

  25. L. Ai, X. Gao, J. Xiong, Application of mean-shift clustering to Blood oxygen level dependent functional MRI activation detection. BMC Med. Imaging 14, 6 (2014)

    Article  Google Scholar 

  26. S. Saha, A.K. Alok, A. Ekbal, Brain image segmentation using semi-supervised clustering. Expert Syst. Appl. 52, 50–63 (2016)

    Article  Google Scholar 

  27. M. Rastgarpour, J. Shanbehzadeh, H. Soltanian-Zadeh, A hybrid method based on fuzzy clustering and local region-based level set for segmentation of inhomogeneous medical images. J. Med. Syst. 38, 1–15 (2014)

    Article  Google Scholar 

  28. I. Ziedan, A. Zamel, A.A. Zohairy, Clustering of medical X-ray images by merging outputs of different classification techniques, in CLEF (2015)

    Google Scholar 

  29. T. Schultz, G.L. Kindlmann, Open-box spectral clustering: applications to medical image analysis. IEEE Trans. Visual. Comput. Graph. 19, 2100–2108 (2013)

    Article  Google Scholar 

  30. C. Kuo, P.B. Walker, O.T. Carmichael, I. Davidson, Spectral clustering for medical imaging, in 2014 IEEE International Conference on Data Mining (2014), pp. 887–892

    Google Scholar 

  31. M.K. Aouf, L. Lyanage, S. Hansen, Review of data mining clustering techniques to analyze data with high dimensionality as applied in gene expression data (June 2008), in 2008 International Conference on Service Systems and Service Management (2008, June), pp. 1–5

    Google Scholar 

  32. S. Datta, S. Datta, Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19(4), 459–66 (2003)

    Article  Google Scholar 

  33. T.L. Bailey, C. Elkan, Fitting a mixture model by expectation maximization to discover motifs in biopolymers, in Proceedings. International Conference on Intelligent Systems for Molecular Biology, vol. 2 (1994), pp. 28–36

    Google Scholar 

  34. J. Oyelade, I. Isewon, F. Oladipupo, O. Aromolaran, E. Uwoghiren, F. Ameh, M. Achas, E. Adebiyi, Clustering algorithms: their application to gene expression data. Bioinform. Biol. Insights (2016)

    Google Scholar 

  35. Y. Meng, X. Liu, Application of K-means algorithm based on ant clustering algorithm in macroscopic planning of highway transportation hub, in 2007 First IEEE International Symposium on Information Technologies and Applications in Education (2007), pp. 483–488

    Google Scholar 

  36. Z. Zhou, G. Si, J. Chen, K. Zheng, W. Yue, A novel method of transformer fault diagnosis based on k-mediods and decision tree algorithm, in 2017 1st International Conference on Electrical Materials and Power Equipment (ICEMPE) (2017), pp. 369–373

    Google Scholar 

  37. X. Peng, C. Zhou, D.M. Hepburn, M.D. Judd, W.H. Siew, Application of K-means method to pattern recognition in on-line cable partial discharge monitoring. IEEE Trans. Dielectr. Electr. Insul. 20, 754–761 (2013)

    Article  Google Scholar 

  38. A. Pomente, D. Aleandri, Convolutional expectation maximization for population estimation, in CLEF (2017)

    Google Scholar 

  39. J. Erman, M.F. Arlitt, A. Mahanti, Traffic classification using clustering algorithms, in MineNet (2006)

    Google Scholar 

  40. S. Bandyopadhyay, E.J. Coyle, An energy efficient hierarchical clustering algorithm for wireless sensor networks, in INFOCOM (2003)

    Google Scholar 

  41. J. Vaidya, C. Clifton, Privacy-preserving k-means clustering over vertically partitioned data, in KDD (2003)

    Google Scholar 

  42. Z. Erkin, T. Veugen, T. Toft, R.L. Lagendijk, Privacy-preserving distributed clustering. EURASIP J. Inf. Secur. 2013, 4 (2013)

    Article  Google Scholar 

  43. J. Drew, T. Moore, Automatic identification of replicated criminal websites using combined clustering, in 2014 IEEE Security and Privacy Workshops (2014), pp. 116–123

    Google Scholar 

  44. M. Ahmed, A.N. Mahmood, J. Hu, A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19–31 (2016)

    Article  Google Scholar 

  45. B. Biggio, K. Rieck, D. Ariu, C. Wressnegger, I. Corona, G. Giacinto, F. Roli, Poisoning behavioral malware clustering, in AISec@CCS (2014)

    Google Scholar 

  46. M. Ishida, H. Takakura, Y. Okabe, High-performance intrusion detection using optigrid clustering and grid-based labelling, in 2011 IEEE/IPSJ International Symposium on Applications and the Internet (2011), pp. 11–19

    Google Scholar 

  47. L. Guo, L. Chen, C.L. Chen, Image guided fuzzy clustering for image segmentation, in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2016), pp. 004271–004276

    Google Scholar 

  48. S. Choy, S.Y. Lam, K.W. Yu, W.Y. Lee, K.T. Leung, Fuzzy model-based clustering and its application in image segmentation. Pattern Recognit. 68, 141–157 (2017)

    Article  Google Scholar 

  49. N. Li, H. Huo, Y. Zhao, X. Chen, T. Fang, A spatial clustering method with edge weighting for image segmentation. IEEE Geosci. Remote Sens. Lett. 10, 1124–1128 (2013)

    Article  Google Scholar 

  50. S. Kim, C.D. Yoo, S. Nowozin, P. Kohli, Image segmentation using higher-order correlation clustering. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1761–1774 (2014)

    Article  Google Scholar 

  51. A. Alush, J. Goldberger, Hierarchical image segmentation using correlation clustering. IEEE Trans. Neural Netw. Learn. Syst. 27, 1358–1367 (2015)

    Article  MathSciNet  Google Scholar 

  52. M.V. Kharinov, Hierarchical pixel clustering for image segmentation (2014). CoRR, http://arxiv.org/abs/1401.5891

  53. M.V. Kharinov, Pixel clustering for color image segmentation. Program. Comput. Softw. 41, 258–266 (2015)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Engineering & Management, Salt Lake, Kolkata, India

    Attri Ghosal, Arunima Nandy & Amit Kumar Das

  2. Cognizant Technology Solutions, Kolkata, India

    Saptarsi Goswami & Mrityunjoy Panday

Authors
  1. Attri Ghosal
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  2. Arunima Nandy
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  3. Amit Kumar Das
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  4. Saptarsi Goswami
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  5. Mrityunjoy Panday
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Correspondence to Arunima Nandy .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India

    Debika Bhattacharya

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Ghosal, A., Nandy, A., Das, A.K., Goswami, S., Panday, M. (2020). A Short Review on Different Clustering Techniques and Their Applications. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_9

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  • DOI: https://doi.org/10.1007/978-981-13-7403-6_9

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