Journal of Medical Systems

, Volume 35, Issue 5, pp 845–853 | Cite as

Evaluating Cluster Preservation in Frequent Itemset Integration for Distributed Databases

  • Sumeet Dua
  • Michael P. Dessauer
  • Prerna Sethi
Original Paper


Medical sciences are rapidly emerging as a data rich discipline where the amount of databases and their dimensionality increases exponentially with time. Data integration algorithms often rely upon discovering embedded, useful, and novel relationships between feature attributes that describe the data. Such algorithms require data integration prior to knowledge discovery, which can lack the timeliness, scalability, robustness, and reliability of discovered knowledge. Knowledge integration algorithms offer pattern discovery on segmented and distributed databases but require sophisticated methods for pattern merging and evaluating integration quality. We propose a unique computational framework for discovering and integrating frequent sets of features from distributed databases and then exploiting them for unsupervised learning from the integrated space. Assorted indices of cluster quality are used to assess the accuracy of knowledge merging. The approach preserves significant cluster quality under various cluster distributions and noise conditions. Exhaustive experimentation is performed to further evaluate the scalability and robustness of the proposed methodology.


Knowledge merging Frequent patterns Clustering Quality indices Distributed databases 


  1. 1.
    Deeray, T., and Verhayden, P. Towards a semantic integration of medical relational databases by using ontologies: a case study. On the Move to Meaningful Internet System 2003 Workshop (OTM ’03), Lecture Notes in Computer Sciences 2889, pp. 137–150, 2003Google Scholar
  2. 2.
    Hadzic, M., and Chang, E., Onto-agent methodology for design of ontology-based mufti-agent systems. Int. J. Comput. Syst. Sci. Eng. 23:19–30, 2008.Google Scholar
  3. 3.
    Batini, C., Lenzerini, M., and Navathe, S. B., A comparative analysis of methodologies for database schema integration. ACM Comput. Surv. 18:323–364, 1986.CrossRefGoogle Scholar
  4. 4.
    Piatetsky-Shapiro, G., Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro, G., and Frawley, W. J. (Eds.), Knowledge Discovery in Databases. AAAI/MIT Press, Cambridge, 1991.Google Scholar
  5. 5.
    Goethals, B., Survey on Frequent Pattern Mining. Available at∼jebara/6772/papers/SurveyFPMining.pdf, 2003.
  6. 6.
    Dua, S., Jain, V., and Thompson, H. W., Patient classification using association mining of clinical images. Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on, pp.253–256, 14–17 May 2008.Google Scholar
  7. 7.
    Zaki, M. J., Parthsarathy, S., Ogihara, M., and Li, W., New algorithms for fast discovery of association rules. KDD, pp. 283–286, 1997.Google Scholar
  8. 8.
    Lent, B., Swami, A., and Widom, J., Clustering association rules. Proc. 1997 Int’l Conf. Data Eng., pp. 220–231, Apr. 1997.Google Scholar
  9. 9.
    Agrawal, R., and Srikant, R., Fast algorithms for mining association rules in large databases. VLDB ’94: Proceedings of the 20th International Conference on Very Large Data Bases. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., pp. 487–499, 1994.Google Scholar
  10. 10.
    Sethi, P., and Jain M., A comparative feature selection approach for the prediction of healthcare coverage. Communications in Computer and Information Science, to appear 2010.Google Scholar
  11. 11.
    Delen, D., Fuller, C., McCann, C., and Ray, D., Analysis of healthcare coverage: a data mining approach. Exp. Syst. Appl. 36:995–1003, 2009.CrossRefGoogle Scholar
  12. 12.
    Dua, S., Singh, H., and Thompson, H. W., Associative classification of mammograms using weighted rules. Exp. Syst. Appl. 36(5):9250–9259, 2009.CrossRefGoogle Scholar
  13. 13.
    Han, J., Pei, H., and Yin, Y., Mining frequent patterns without candidate generation. In: Proc. conf. on the Management of Data (SIGMOD’00, Dallas, TX). ACM Press, New York, 2000.Google Scholar
  14. 14.
    Sethi, P., and Leangsuksun, C., A novel computational framework for fast distributed computing and knowledge integration for microarray gene expression data analysis. Advanced Information Networking and Applications, International Conference on, pp. 613–617, 20th International Conference on Advanced Information Networking and Applications - Volume 2 (AINA’06), 2006.Google Scholar
  15. 15.
    Rand, W. M., Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66:846–850, 1971.CrossRefGoogle Scholar
  16. 16.
    Hubert, L., and Arabie, P., Comparing partitions. J. Classif. 193–218, 1985.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Sumeet Dua
    • 1
  • Michael P. Dessauer
    • 1
  • Prerna Sethi
    • 2
  1. 1.Data Mining Research Laboratory, Department of Computer ScienceLouisiana Tech UniversityRustonUSA
  2. 2.Department of Health Informatics and Information ManagementLouisiana Tech UniversityRustonUSA

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