Abstract
Nowadays, with the evolution of the data in data processing and storage of great volumes of these diversified data, the software of Data Mining became without context a necessity for the majority of the users of the Information Systems. Unfortunately, currently marketed software are very limited and don’t meet all user needs. This software supports only some classification algorithms and some Knowledge Discovery in Databases (KDD) algorithms that generate a big number of rules which are not understandable by the end user. Moreover, these approaches are applicable only for restricted data type. In this paper, we propose new software of classification and KDD, called Cluster-KDD, which supports a larger set of data type and classification algorithm and offers KDD algorithms that generate comprehensible and exploitable rules by the user.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Goebel, M., Gruenwald, L.: A Survey of Data Mining and Knowledge Discovery Software Tools. ACM SIGKDD 1(1), 20–33 (1999)
Zaki, M.: Mining Non-Redundant Association Rules. Data Mining and Knowledge Discovery (9), 223–248 (2004)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between sets of items in large Databases. In: Proceedings of the ACM SIGMOD Intl. Conference on Management of Data, Washington, USA, pp. 207–216 (June 1993)
Agrawal, R., Skirant, R.: Fast algoritms for mining association rules. In: Proceedings of the 20th Int’l Conference on Very Large Databases, pp. 478–499 (June 1994)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient Mining of Association Rules Using Closed Itemset Lattices. Information Systems Journal 24(1), 25–46 (1999)
Zaki, M.J., Hsiao, C.J.: CHARM: An Efficient Algorithm for Closed Itemset Mining. In: Proceedings of the 2nd SIAM International Conference on Data Mining, Arlington, pp. 34–43 (April 2002)
Kantardzic, M.: Data mining: concepts, models, methods, and algorithms. Wiley-IEEE Press (2011)
McMueen, J.: Some methods for classiffication and analysis of multivariate observations. In: The Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Bezdek, J.: Fuzzy mathematics in pattern classification. Ph.D. Dissertation, Cornell University (1973)
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: An efficient data clustering method for very large database. In: ACM-SIGMOD Int. Conf. Managerment of Data, Montreal, Canada, pp. 103–114 (1996)
Guha, S., Rastogi, R., Shi, K.: CURE: an efficient clustering algorithm for large databases. In: ACM SIGMOD Int ’l Conf. Management of Data, pp. 73–84 (1998)
Ester, M., Kriegel, H., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: 3rd Int. Conf. Knowledge Discovery and Data Mining (KDD 1996), pp. 232–237 (1997)
Sheikholeslami, J.C., Chatterjee, S., Zhang, A.: Wave: cluster: A multiresolution clustering approach for very large special database. In: Int. Conf. Very Large Database (VLDB 1998), NY, USA, pp. 428–439 (1998)
Grissa Touzi, A., Thabet, A., Sassi, M.: Efficient Reduction of the Number of Associations Rules Using Fuzzy Clustering on the Data. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part II. LNCS, vol. 6729, pp. 191–199. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Touzi, A.G., Aloui, A., Mahouachi, R. (2012). Cluster_KDD: A Visual Clustering and Knowledge Discovery Platform Based on Concept Lattice. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-31020-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31019-5
Online ISBN: 978-3-642-31020-1
eBook Packages: Computer ScienceComputer Science (R0)