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Two Phase Iterative Clustering for Educational Data

S. M. Karad, Prasad S. Halgaonkar, V. M. Wadhai, Dipti D. Patil, M. U. Kharat Published in Data Mining

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
10.5120/ijais12-450173
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  1. S M Karad, Prasad S Halgaonkar, V M Wadhai, Dipti D Patil and M U Kharat. Article: Two Phase Iterative Clustering for Educational Data. International Journal of Applied Information Systems 1(5):11-15, February 2012. BibTeX

    @article{key:article,
    	author = "S. M. Karad and Prasad S. Halgaonkar and V. M. Wadhai and Dipti D. Patil and M. U. Kharat",
    	title = "Article: Two Phase Iterative Clustering for Educational Data",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 1,
    	number = 5,
    	pages = "11-15",
    	month = "February",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

In the field of data mining, clustering of educational data has not given much of the importance. Considering the growth of educational field as a business, clustering of educational data must be focused as it can give effective results as in the case of mining enrolled students on the basis of education they undertake. A new algorithm is proposed and implemented by us for clustering educational data. This algorithm is based on a continuous looping procedure. Raw dataset is assigned to clustering algorithm initially and a novel cluster is identified for partition whose cluster high degree is less. Then improvement of degree of cluster is carried out. In this algorithm on the basis of homogeneity, cluster high degree is defined. Experiment is carried out on educational data; which provides good high degree clusters.

Reference

  1. J. Grabmeier and A. Rudolph, “Techniques of Cluster Algorithms in Data Mining,” Data Mining and Knowledge Discovery, vol. 6, no. 4, pp. 303-360, 2002.
  2. A. Jain and R. Dubes, Algorithms for Clustering Data. Prentice Hall, 1988.
  3. R. Ng and J. Han, “CLARANS: A Method for Clustering Objects for Spatial Data Mining,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 5, pp. 1003-1016, Sept./Oct. 2002.
  4. A.M. Bagiwa, S.I. Dishing, “A Conceptual Framework for Extending Distance Measure Algorithm For Data Clustering”, International Journal of Computer Trends and Technology- March to April issue.
  5. G. Gan and J. Wu, “Subspace Clustering for High Dimensional Categorical Data,” SIGKDD Explorations, vol. 6, no. 2, pp. 87-94, 2004.
  6. Y. Yang, X. Guan, and J. You, “CLOPE: A Fast and Effective Clustering Algorithm for Transactional Data,” Proc. Eighth ACM Conf. Knowledge Discovery and Data Mining (KDD ’02), pp. 682-687, 2002.
  7. Eugenio Cesario, Giuseppe Manco, and Riccardo Ortale, “Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data”. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 19, NO. 12, DECEMBER 2007.
  8. E. Han, G. Karypis, V. Kumar, and B. Mobasher, “Clustering in a High Dimensional Space Using Hypergraph Models,” Proc. ACM SIGMOD Workshops Research Issues on Data Mining and Knowledge Discovery (DMKD ’97), 1997.
  9. M. Ozdal and C. Aykanat, “Hypergraph Models and Algorithms for Data-Pattern-Based Clustering,” Data Mining and Knowledge Discovery, vol. 9, pp. 29-57, 2004.
  10. K. Wang, C. Xu, and B. Liu, “Clustering Transactions Using Large Items,” Proc. Eighth Int’l Conf. Information and Knowledge Management (CIKM ’99), pp. 483-490, 1999.
  11. P. Andritsos, P. Tsaparas, R. Miller, and K. Sevcik, “LIMBO: Scalable Clustering of Categorical Data,” Proc. Ninth Int’l Conf. Extending Database Technology (EDBT ’04), pp. 123-146, 2004.
  12. I. Cadez, P. Smyth, and H. Mannila, “Probabilistic Modeling of Transaction Data with Applications to Profiling, Visualization, and Prediction,” Proc. Seventh ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’01), pp. 37-46, 2001.
  13. M. Carreira-Perpinan and S. Renals, “Practical Identifiability of Finite Mixture of Multivariate Distributions,” Neural Computation, vol. 12, no. 1, pp. 141-152, 2000.
  14. G. McLachlan and D. Peel, Finite Mixture Models. John Wiley & Sons, 2000.
  15. C. Fraley and A. Raftery, “How Many Clusters? Which Clustering Method? The Answer via Model-Based Cluster Analysis,” The Computer J., vol. 41, no. 8, 1998.
  16. P. Smyth, “Model Selection for Probabilistic Clustering Using Cross-Validated Likelihood,” Statistics and Computing, vol. 10, no. 1, pp. 63-72, 2000.
  17. D. Pelleg and A. Moore, “X-Means: Extending K-Means with Efficient Estimation of the Number of Clusters,” Proc. 17th Int’l Conf. Machine Learning (ICML ’00), pp. 727-734, 2000.
  18. M. Sultan et al., “Binary Tree-Structured Vector Quantization Approach to Clustering and Visualizing Microarray Data,” Bioinformatics, vol. 18, 2002.
  19. S. Guha, R. Rastogi, and K. Shim, “ROCK: A Robust Clustering Algorithm for Categorical Attributes,” Information Systems, vol. 25, no. 5, pp. 345-366, 2001.
  20. J. Basak and R. Krishnapuram, “Interpretable Hierarchical Clustering by Constructing an Unsupervised Decision Tree,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 1, Jan. 2005.
  21. Yi-Dong Shen, Zhi-Yong Shen and Shi-Ming Zhang,“Cluster Cores – based Clustering for High – Dimensional Data”.
  22. Alexander Hinneburg and Daniel A. Keim, Markus Wawryniuk,“HD-Eye-Visual of High-Dimensional Data: A Demonstration”.

Keywords

Clustering, Cluster homogeneity, Educational Data