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Abstract
Data mining is having techniques like clustering, classification and algorithm like frequent itemset mining algorithms. In this we discuss about clustering and classification techniques with its applications and frequent itemset mining algorithm with its applications.
Cluster is the process for set of objects in cluster that objects are nearer to the center of cluster and far to other cluster and clustering is used to place the data elements into groups. Classification is the collection of records as training set and each record is having attributes. Frequent itemset algorithms are used for mining the frequent items of the transactions of the databases.
Index Terms— Data Mining, Clustering, Classification, Frequent itemset mining, Data Mining Applications.##plugins.themes.academic_pro.article.details##
How to Cite
D.R.N.Sravana Lakshmi, Z. B. (2015). Classification of Clustering with Frequent item-sets. International Journal of Emerging Trends in Science and Technology, 2(02). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/521
References
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13. learning tools and techniques", 2nd Edition, Morgan Kaufmann,San Francisco, 2005
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16. R. Agarwal, C. Aggarwal, and V. V. V. Prasad: A Tree Projection Algorithm for Generation of Frequent Itemsets. Journal of Parallel and Distributed Computing (special issue on high performance data mining), (to appear), 2000.
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19. Wilson, D. R. & Martinez, T. (2000). Reduction Techniques for Instance-Based Learning Algorithms. Machine Learning 38:257–286.
20. Witten, I. & Frank, E. (2005), "Data Mining: Practical machine
2. Breslow, L. A. & Aha, D. W. (1997). Simplifying decision trees: A survey. Knowledge Engineering Review 12: 1–40.
3. Baik, S. Bala, J. (2004), A Decision Tree Algorithm for Distributed Data Mining: Towards Network Intrusion Detection, Lecture Notes in Computer Science, Volume 3046, Pages 206 – 212.
4. Baik, S. Bala, J. (2004), A Decision Tree Algorithm for Distributed Data Mining: Towards Network Intrusion Detection, Lecture Notes in Computer Science, Volume 3046,Pages 206 – 212.
5. Cheng, J., Greiner, R., Kelly, J., Bell, D., & Liu, W. (2002). Learning Bayesian networks from data: An information-theory based approach Artificial Intelligence 137: 43–90.
6. Clark, P., Niblett, T. (1989), The CN2 Induction Algorithm. Machine Learning, 3(4):261-283.
7. Digital Image Processing and Analysis-byB.Chanda and D.Dutta Majumdar.
8. Guha S., Rastogi R., Shim K.: ―CURE: An Efficient Clustering Algorithms for Large Databases‖, P-oc. ACM SIGMOD Int. Conf. on Management of Data, Seattle, WA,1998, pp. 73-84.
9. J. Han, H. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. Proc. Conf. on the Management of Data (SIGMOD‘00, Dallas, TX), 1–12. ACM Press, New York, NY, USA 2000.
10. Jain A. K., Dubes R. C.: ―Algorithms for Clustering Data,‖Prentice-Hall, Inc., 1988.
11. M. Ester, H. P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining (KDD-96), pp. 226-231, Portland, OR, USA, August 1996.
12. M. S. Chen, J. Han, P. S. Yu. Data mining: an overview from database perspective. To appear in IEEE Transactions on Knowledge and data Engineering, 1997.
13. learning tools and techniques", 2nd Edition, Morgan Kaufmann,San Francisco, 2005
14. J. A. Hartigan and M. A. Wong (1979) "A K- Means Clustering Algorithm", Applied Statistics, Vol. 28, No. 1, p100-108
15. H. Zha, C. Ding, M. Gu, X. He and H.D. Simon. "Spectral Relaxation for K-means Clustering", Neural Information Processing Systems vol.14 (NIPS 2001). pp. 1057-1064, Vancouver, Canada. Dec. 2001.
16. R. Agarwal, C. Aggarwal, and V. V. V. Prasad: A Tree Projection Algorithm for Generation of Frequent Itemsets. Journal of Parallel and Distributed Computing (special issue on high performance data mining), (to appear), 2000.
17. R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. Proceedings. 20th Int. Conf. on very Large Databases (VLDB 1994, Santiago de Chile), 487–499. Morgan Kaufmann, San Mateo, CA, USA 1994.
18. S. Azad Razvi, Mr. S. Vikram Phaneendra|, Concise Range Queries with Efficient and Optimal Representation‖, IFRSA‘s International Journal Of Computing,Vol2,issue 3,July 2012, pp 657 - 662.
19. Wilson, D. R. & Martinez, T. (2000). Reduction Techniques for Instance-Based Learning Algorithms. Machine Learning 38:257–286.
20. Witten, I. & Frank, E. (2005), "Data Mining: Practical machine