MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING

Alejandro García López, Rafael Berlanga, Roxana Danger

Abstract

In this work we present a formal framework for mining complex objects, being those characterised by a set of heterogeneous attributes and their corresponding values. First we will do an introduction of the various Data Mining techniques available in the literature to extract association rules. We will as well show some of the drawbacks of these techniques and how our proposed solution is going to tackle them. Then we will show how applying a clustering algorithm as a pre-processing step on the data allow us to find groups of attributes and objects that will provide us with a richer starting point for the Data Mining process. Then we will define the formal framework, its decision functions and its interesting measurement rules, as well as a newly designed Data Mining algorithms specifically tuned for our objectives. We will also show the type of knowledge to be extracted in the form of a set of association rules. Finally we will state our conclusions and propose the future work.

References

  1. Agrawal, R., Imielinski, T., and Swami, A. N. (1993). Mining association rules between sets of items in large databases. In Buneman, P. and Jajodia, S., editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207-216, Washington, D.C.
  2. Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. In Bocca, J. B., Jarke, M., and Zaniolo, C., editors, Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pages 487-499. Morgan Kaufmann.
  3. Aslam, J. A., Pelekhov, K., and Rus, D. (1998). Static and dynamic information organization with star clusters. In CIKM, pages 208-217.
  4. Aumann, Y. and Lindell, Y. (1999). A statistical theory for quantitative association rules. In KDD, pages 261- 270.
  5. Danger, R., Ruiz-Shulcloper, J., and Berlanga, R. (2004). Objectminer: A new approach for mining complex objects. In ICEIS (2), pages 42-47.
  6. Dong, L. and Tjortjis, C. (2003). Experiences of using a quantitative approach for mining association rules. In IDEAL, pages 693-700.
  7. Gil-García, R., Badía-Contelles, J. M., and Pons-Porrata, A. (2003). Extended star clustering algorithm. In CIARP, pages 480-487.
  8. Gyenesei, A. (2000). Mining weighted association rules for fuzzy quantitative items. In PKDD 7800: Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pages 416- 423, London, UK. Springer-Verlag.
  9. Hipp, J., Myka, A., Wirth, R., and Güntzer, U. (1998). A new algorithm for faster mining of generalized association rules. In Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD 7898), pages 74-82, Nantes, France.
  10. Kann, V. (1999). A compendium of NP optimization problems. In Complexity and Aproximation. Springer Verlag.
  11. Kuok, C. M., Fu, A. W.-C., and Wong, M. H. (1998). Mining fuzzy association rules in databases. SIGMOD Record, 27(1):41-46.
  12. Miller, R. J. and Yang, Y. (1997). Association rules over interval data. pages 452-461.
  13. Okoniewski, M., Gancarz, L., and Gawrysiak, P. (2001). Mining multi-dimensional quantitative associations. In INAP, pages 265-274.
  14. Park, J. S., Chen, M.-S., and Yu, P. S. (1995). An effective hash based algorithm for mining association rules. In Carey, M. J. and Schneider, D. A., editors, Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, pages 175-186, San Jose, California.
  15. Srikant, R. and Agrawal, R. (1996). Mining quantitative association rules in large relational tables. In Jagadish, H. V. and Mumick, I. S., editors, Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pages 1-12, Montreal, Quebec, Canada.
  16. Srikant, R. and Agrawal, R. (1997). Mining generalized association rules. volume 13, pages 161-180.
  17. Tong, Q., Yan, B., and Zhou, Y. (2005). Mining quantitative association rules on overlapped intervals. In ADMA, pages 43-50.
  18. Z. Zhing, Y. L. and Zhang, B. (1997). An effective partitioning-combining algorithm for discovering quantitative association rules. In Proc. of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining.
Download


Paper Citation


in Harvard Style

García López A., Berlanga R. and Danger R. (2006). MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING . In Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT, ISBN 978-972-8865-69-6, pages 187-194. DOI: 10.5220/0001318401870194


in Bibtex Style

@conference{icsoft06,
author={Alejandro García López and Rafael Berlanga and Roxana Danger},
title={MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING},
booktitle={Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT,},
year={2006},
pages={187-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001318401870194},
isbn={978-972-8865-69-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT,
TI - MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING
SN - 978-972-8865-69-6
AU - García López A.
AU - Berlanga R.
AU - Danger R.
PY - 2006
SP - 187
EP - 194
DO - 10.5220/0001318401870194