Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 03, March 2024

Call for Paper

Vol 11, Issue 03, March 2024

Indexing

Big Data Clustering Algorithms - A Survey

Author : Mr. Roshansingh P. Thakur 1 Ms. Mitali R. Ingle 2 Mr. Dinesh S. Gawande 3

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Date of Publication :7th February 2017

Abstract: Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. There is a vast body of knowledge in the area of clustering and there have been attempts to analyze and categorize them for a larger number of applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a hypothetical and an analytical perspective. From a hypothetical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Analytically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data. Clustering algorithms have metamorphose an extra powerful meta-learning instrument to accurately study the huge volume of data generated by hot off the fire applications. In disparate, their dominant goal is to recognize data into clusters one that objects are grouped in the much the comparable cluster when they are evocative according to unwavering metrics. There is a vast advantage of lifestyle in the trend of clustering and there have been attempts to equal and categorize them for a larger zip code of applications. However, a well known of the masterpiece issues in via clustering algorithms for big data that causes guilt amongst practitioners is the call for of common consent in the language of their properties as well as a call for of reserved categorization. With the future of alleviating these problems, this paper introduces concepts and algorithms thick to clustering, a compendious survey of critical (clustering) algorithms as well as providing a allegory, both from a hypo thetical and an analytical perspective. From a hypo thetical where one is at, we swollen a categorizing frame of reference based on the dominating properties concise out in immediate studies. Analytically, we conducted bountiful experiments to what place we compared the close but no cigar representative algorithm separately of the categories by a wealthy abode of real (big) data sets. The efficiency of the team member clustering algorithms is measured over a number of internal and external validity metrics, toughness, runtime, and scalability tests. In presentation, we highlighted the fit of clustering algorithms that are the outstanding performing for big data.

Reference :

    1. Abbasi and M. Younis, ``A survey on clustering algorithms for wireless sensor networks,'' Comput. Commun., vol. 0, nos. 14_15, pp. 2826_2841, Oct. 2007.
    2. C. C. Aggarwal and C. Zhai, ``A survey of text clustering algorithms,'' in Mining Text Data. New York, NY, USA: Springer-Verlag, 2012, pp. 77_128.
    3. J. Brank, M. Grobelnik, and D. Mladeni¢, ``Asurvey of ontology evaluation techniques,'' in Proc. Conf. Data Mining DataWarehouses (SiKDD), 2005.
    4. R. Xu and D. Wunsch, ``Survey of clustering algorithms,'' IEEE Trans. Neural Netw., vol. 16, no. 3, pp. 645_678, May 2005.
    5. J. C. Bezdek, R. Ehrlich, andW. Full, ``FCM: The fuzzy c-means clustering algorithm,'' Comput. Geosci., vol. 10, nos. 2_3, pp. 191_203, 1984.
    6. T. Zhang, R. Ramakrishnan, and M. Livny, ``BIRCH: An ef_cient data clustering method for very large databases,'' in Proc. ACM SIGMOD Rec., Jun. 1996, vol. 25, no. 2, pp. 103_114.
    7. A. Hinneburg and D. A. Keim, ``An ef_cient approach to clustering in large multimedia databases with noise,'' in Proc. ACM SIGKDD Conf. Knowl. Discovery Ad Data Mining (KDD), 1998, pp. 58_65.
    8. Alexander Hinneburg, Er Hinneburg, and Daniel A. Keim. An efficient approach to clustering in large multimedia databases with noise. In Proc. 4rd Int. Conf. on Knowl- edge Discovery and Data Mining, pages 58–65. AAAI Press, 1998.
    9. Alexander Hinneburg and Daniel A. Keim. Optimal grid-clustering: Towards breaking the curse of dimensionality in high-dimensional clustering. In VLDB'99, pages 506–517. Morgan Kaufmann, 1999
    10. A. P. Dempster, N. M. Laird, and D. B. Rubin, ``Maximum likelihood from incomplete data via the em algorithm,'' J. Roy. Statist. Soc., Ser. B, vol. 39, no. 1, pp. 1_38, 1977.
    11. M. Meil and D. Heckerman, ``An experimental comparison of several clustering and initialization methods,'' in Proc. 14th Conf. Uncertainty Artif. Intell. (UAI), 1998, pp. 386_395.
    12. J. Brank, M. Grobelnik, and D. Mladeni¢, ``Asurvey of ontology evaluation techniques,'' in Proc. Conf. Data Mining DataWarehouses (SiKDD), 2005.
    13. A. P. Dempster, N. M. Laird, and D. B. Rubin, ``Maximum likelihood from incomplete data via the em algorithm,'' J. Roy. Statist. Soc., Ser. B, vol. 39, no. 1, pp. 1_38, 1977.
    14. S. Guha, R. Rastogi, and K. Shim, ``Cure: An ef_cient clustering algorithm for large databases,'' in Proc. ACMSIGMOD Rec., Jun. 1998, vol. 27, no. 2, pp. 73_84.
    15. S. Guha, R. Rastogi, and K. Shim, ``Rock: A robust clustering algorithm for categorical attributes,'' Inform. Syst., vol. 25, no. 5, pp. 345_366, 2000.
    16. J. Han and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed. San Mateo, CA, USA: Morgan Kaufmann, 2006.
    17. A. Hinneburg and D. A. Keim, ``An ef_cient approach to clustering in large multimedia databases with noise,'' in Proc. ACM SIGKDD Conf. Knowl. Discovery Ad Data Mining (KDD), 1998, pp. 58_65.
    18. Hinneburg and D. A. Keim, ``Optimal gridclustering: Towards breaking the curse of dimensionality in high-dimensional clustering,'' in Proc. 25th Int. Conf. Very Large Data Bases (VLDB), 1999, pp. 506_517.
    19. Z. Huang, ``A fast clustering algorithm to cluster very large categorical data sets in data mining,'' in Proc. SIGMODWorkshop Res. Issues Data Mining Knowl. Discovery, 1997, pp. 1_8.
    20. L. Hubert and P. Arabie, ``Comparing partitions,'' J. Classi_cation, vol. 2, no. 1, pp. 193_218, 1985.
    21. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Upper Saddle River, NJ, USA: Prentice-Hall, 1988.
    22. G. Karypis, E.-H. Han, andV.Kumar, ``Chameleon: Hierarchical clustering using dynamic modelling,'' IEEE Comput., vol. 32, no. 8, pp. 68_75, Aug. 1999.
    23. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. New York, NY, USA: Wiley, 2009
    24. T. Kohonen, ``The self-organizing map,'' Neurocomputing, vol. 21, no. 1, pp. 1_6, 1998
    25. J. MacQueen, ``Some methods for classi_cation and analysis of multivariate observations,'' in Proc. 5th Berkeley Symp. Math. Statist. Probab. Berkeley, CA, USA, 1967, pp. 281_297.
    26. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, ``A density-based algorithm for discovering clusters in large spatial databases with noise,'' in Proc. ACM SIGKDD Conf. Knowl. Discovery Ad Data Mining (KDD), 1996, pp. 226_231.
    27. A. Fahad, Z. Tari, A. Almalawi, A. Goscinski, I. Khalil, and A. Mahmood, ``PPFSCADA: Privacy preserving framework for SCADA data publishing,'' Future Generat. Comput. Syst., vol. 37, pp. 496_511, Jul. 2014.
    28. Fahad, Z. Tari, I. Khalil, I. Habib, and H. Alnuweiri, ``Toward an ef_cient and scalable feature selection approach for internet traf_c classi _cation,'' Comput. Netw., vol. 57, no. 9, pp. 2040_2057, Jun. 2013.
    29. H. Fisher, ``Knowledge acquisition via incremental conceptual clustering,'' Mach. Learn., vol. 2, no. 2, pp. 139_172, Sep. 1987.
    30. J. H. Gennari, P. Langley, and D. Fisher, ``Models of incremental concept formation,'' Artif. Intell., vol. 40, nos. 1_3, pp. 11_61, Sep. 1989.

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