Association Rule Construction from Crime Pattern Through Novelty Approach

  • D. UshaEmail author
  • K. Rameshkumar
  • B. V. Baiju
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)


The objective of association rule mining is to mine interesting relationships, frequent patterns, associations between set of objects in the transaction database. In this paper, association rule is constructed from the proposed rule mining algorithm. Efficiency-based association rule mining algorithm is used to generate patterns and Rule Construction algorithm is used to form association among the generated patterns. This paper aims at applying crime dataset, from which frequent items are generated and association made among the frequent item set. It also compares the performance with other existing rule mining algorithm. The algorithm proposed in this paper overcomes the drawbacks of the existing algorithm and proves the efficiency in minimizing the execution time. Synthetic and real datasets are applied with the rule mining algorithm to check the efficiency and it proves the results through experimental analysis.


ARM IRM Rule construct Crime dataset Information gain 


  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  2. 2.
    Association Rule.
  3. 3.
    Krishnamurthy, R., Satheesh Kumar, J.: Survey of data mining techniques on crime data analysis. Int. J. Data Mining Tech. Appl. 1(2), 117–120 (2012)Google Scholar
  4. 4.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, pp. 207–216 (1993)Google Scholar
  5. 5.
    Bhandari, P., et al.: Improved apriori algorithms—A survey. Int. J. Adv. Comput. Eng. Netw. 1(2) (2013)Google Scholar
  6. 6.
    Rule Mining Algorithms.
  7. 7.
    Constraint based association rule mining algoritnm.
  8. 8.
    Rule Based Association Rule Mining algorithm [online] Available at
  9. 9.
    Attribute Selection.
  10. 10.
    Information Gain.
  11. 11.
    Azhagusundari, B., Thanamani, A.S.: Feature selection based on information gain. Int. J. Innovative Technol. Exploring Eng. (IJITEE) 2(2), 18–21 (2013). ISSN 2278-3075Google Scholar
  12. 12.
    Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 359–366 (2000)Google Scholar
  13. 13.
  14. 14.
  15. 15.
    Biesiada, J., Duch, W., Duch, G.: Feature selection for high-dimensional data: a Kolmogorov-Smirnov correlation-based filter. In: Proceedings of the International Conference on Computer Recognition Systems (2005)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Dr. M.G.R.Educational and Research InstituteChennaiIndia
  2. 2.Hindustan Institute of Technology and ScienceChennaiIndia

Personalised recommendations