Framework for High Utility Pattern Mining using Dynamically Generated Minimum Support ThresholdFramework for High Utility Pattern Mining using Dynamically Generated Minimum Support Threshold

  • Authors

    • Shankar B. Naik
    • Jyoti D. Pawar
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.28276
  • Data mining, high utility itemset, data stream, closed itemset, frequent itemset.

  • In this paper we have proposed a framework which uses high utility itemset mining to store data stream elements in a compressed form and then detect events from the sliding window. This approach promises to reduce the memory requirements when applied to frequent pattern mining in data streams.

    In addition to this, a method to dynamically define the value of minimum support threshold based on data in the data stream is presented.

     

     

  • References

    1. [1] https://www.kaggle.com.

      [2] Rakesh Agrawal, Tomasz Imielin´ski, and Arun Swami. Mining association rules between sets of items in large databases. In Acm sigmod record, volume 22, pages 207–216. ACM, 1993.

      [3] Brian Babcock, Shivnath Babu, Mayur Datar, Rajeev Motwani, and Jennifer Widom. Models and issues in data stream systems. In Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 1–16. ACM, 2002.

      [4] Jiawei Han, Hong Cheng, Dong Xin, and Xifeng Yan. Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 15(1):55–86, 2007.

      [5] Jiawei Han, Jian Pei, Yiwen Yin, and Runying Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data mining and knowledge discovery, 8(1):53–87, 2004.

      [6] Naik, S. B., & Pawar, J. D. (2013, December). An efficient incremental algorithm to mine closed frequent itemsets over data streams. In Proceedings of the 19th International Conference on Management of Data (pp. 117-120). Computer Society of India.

      [7] Naik, S. B., & Pawar, J. D. (2015, March). A quick algorithm for incremental mining closed frequent itemsets over data streams. In Proceedings of the Second ACM IKDD Conference on Data Sciences (pp. 126-127). ACM.

      [8] Matthijs Van Leeuwen and Arno Siebes. Streamkrimp: Detecting change in data streams. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 672–687. Springer, 2008.

      [9] Xintian Yang, Amol Ghoting, Yiye Ruan, and Srinivasan Parthasarathy. A framework for summarizing and analyzing twitter feeds. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 370–378. ACM, 2012.

      [10] Naik, S. B., & Pawar, J. D. (2012). Finding frequent item sets from data streams with supports estimated using trends. Journal of Information and Operations Management, 3(1), 153.

      [11] Naik, S. B., & Pawar, J. D. (2017, May). Clustering attribute values in transitional data streams. In Computing, Communication and Automation (ICCCA), 2017 International Conference on (pp. 58-62). IEEE.

      [12] Naik, S. B., & Pawar, J. D. (2017, July). A single-pass algorithm for incremental mining patterns over data streams. In Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2017 International Conference on (pp. 565-569). IEEE.

      [13] Naik, S. B., & Pawar, J. D. (2017, June). Mining association rules between values across attributes in data streams. In Computational Intelligence in Data Science (ICCIDS), 2017 International Conference on (pp. 1-6). IEEE.

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  • How to Cite

    B. Naik, S., & D. Pawar, J. (2018). Framework for High Utility Pattern Mining using Dynamically Generated Minimum Support ThresholdFramework for High Utility Pattern Mining using Dynamically Generated Minimum Support Threshold. International Journal of Engineering & Technology, 7(4.19), 1007-1010. https://doi.org/10.14419/ijet.v7i4.19.28276