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Cluster_KDD: A Visual Clustering and Knowledge Discovery Platform Based on Concept Lattice

  • Amel Grissa Touzi
  • Amira Aloui
  • Rim Mahouachi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7332)

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.

Keywords

Data mining Knowledge Discovery in Databases Clustering Formal Concept Analysis Fuzzy Logic 

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References

  1. 1.
    Goebel, M., Gruenwald, L.: A Survey of Data Mining and Knowledge Discovery Software Tools. ACM SIGKDD 1(1), 20–33 (1999)CrossRefGoogle Scholar
  2. 2.
    Zaki, M.: Mining Non-Redundant Association Rules. Data Mining and Knowledge Discovery (9), 223–248 (2004)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Kantardzic, M.: Data mining: concepts, models, methods, and algorithms. Wiley-IEEE Press (2011)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Bezdek, J.: Fuzzy mathematics in pattern classification. Ph.D. Dissertation, Cornell University (1973)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Amel Grissa Touzi
    • 1
  • Amira Aloui
    • 1
  • Rim Mahouachi
    • 1
  1. 1.Ecole Nationale d’Ingénieurs de TunisTunisTunisia

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