An Extended Model of Local Pattern Analysis

  • Animesh AdhikariEmail author
  • Pralhad Ramachandrarao
  • Witold Pedrycz
Part of the Advanced Information and Knowledge Processing book series (AI&KP)


The model of local pattern analysis provides sound solutions to many multi-database mining problems. In this chapter, we will discuss different types of extreme association rules in multiple databases viz., heavy association rule, high-frequency association rule, low-frequency association rule and exceptional association rule. Also, we show how one can apply the model of local pattern analysis more systematically and effectively. For this purpose, we introduce an extended model of local pattern analysis. We apply the extended model to mine heavy association rules in multiple databases. Also, we justify why the extended model works more effectively. We develop an algorithm for synthesizing heavy association rule in multiple databases. Furthermore, we show that the algorithm identifies whether a heavy association rule is high-frequency rule or exceptional rule. We have provided experimental results obtained for both synthetic and real-world datasets and carried out detailed error analysis. Furthermore, we bring a detailed comparative analysis by contrasting the proposed algorithm with some of those reported in the literature. This analysis is completed by taking into consideration the criteria of execution time and average error.


Association Rule Extended Model Local Pattern Frequent Itemsets Association Rule Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Adhikari A, Rao PR (2008) Synthesizing heavy association rules from different real data sources. Pattern Recognition Letters 29(1): 59–71CrossRefGoogle Scholar
  2. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD Conference, Washington, DC, pp. 207–216Google Scholar
  3. Agrawal R, Shafer J (1999) Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering 8(6): 962–969CrossRefGoogle Scholar
  4. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of International Conference on Very Large Data Bases, pp. 487–499Google Scholar
  5. Chattratichat J, Darlington J, Ghanem M, Guo Y, Hüning H, Köhler M, Sutiwaraphun J, To HW, Yang D (1997) Large scale data mining: Challenges, and responses. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 143–146Google Scholar
  6. Cheung D, Ng V, Fu A, Fu Y (1996) Efficient mining of association rules in distributed databases. IEEE Transactions on Knowledge and Data Engineering 8(6): 911–922CrossRefGoogle Scholar
  7. Frequent itemset mining dataset repository (2004)
  8. Han J, Pei J, Yiwen Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD Conference on Management of Data, Dallas, TX, pp. 1–12Google Scholar
  9. Last M, Kandel A (2001) Automated detection of outliers in real-world data. In: Proceedings of the Second International Conference on Intelligent Technologies, Bangkok, pp. 292–301Google Scholar
  10. Pyle D (1999) Data Preparation for Data Mining. Morgan Kufmann, San FranciscoGoogle Scholar
  11. Ramkumar T, Srivinasan R (2008) Modified algorithms for synthesizing high-frequency rules from different data sources. Knowledge and Information Systems 17(3): 313–334CrossRefGoogle Scholar
  12. Rozenberg B, Gudes E (2006) Association rules mining in vertically partitioned databases. Data and Knowledge Engineering 59(2): 378–396CrossRefGoogle Scholar
  13. Savasere A, Omiecinski E, Navathe S (1995) An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st International Conference on Very Large Data Bases, Zurich, Switzerland, pp. 432–443Google Scholar
  14. Shang S, Dong X, Li J, Zhao Y (2008) Mining positive and negative association rules in multi-database based on minimum interestingness. In: Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation 01, Washington, DC, pp. 791–794Google Scholar
  15. Wu X, Zhang S (2003) Synthesizing high-frequency rules from different data sources. IEEE Transactions on Knowledge and Data Engineering 14(2): 353–367Google Scholar
  16. Yi X, Zhang Y (2007) Privacy-preserving distributed association rule mining via semi-trusted mixer. Data and Knowledge Engineering 63(2): 550–567CrossRefGoogle Scholar
  17. Zhang S, Wu X, Zhang C (2003) Multi-database mining. IEEE Computational Intelligence Bulletin 2(1): 5–13Google Scholar
  18. Zhang S, You X, Jin Z, Wu X (2009) Mining globally interesting patterns from multiple databases using kernel estimation. Expert Systems with Applications: An International Journal 36(8): 10863–10869Google Scholar
  19. Zhong N, Yao YYY, Ohishima M (2003) Peculiarity oriented multidatabase mining. IEEE Transactions on Knowledge and Data Engineering 15(4): 952–960CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2010

Authors and Affiliations

  • Animesh Adhikari
    • 1
    Email author
  • Pralhad Ramachandrarao
    • 2
  • Witold Pedrycz
    • 3
  1. 1.Department of Computer ScienceSmt. Parvatibal Chowgule CollegeMargoaIndia
  2. 2.Department of Computer Science & TechnologyGoa UniversityGoaIndia
  3. 3.Department of Electrical & Computer EngineeringUniversity of AlbertaEdmontonCanada

Personalised recommendations