Total views : 201

A Review: Frequent Pattern Mining Techniques in Static and Stream Data Environment

Affiliations

  • Department of Computer Science and Engineering, Lovely Professional University, Phagwara - 144411, Punjab, India

Abstract


Objectives: This paper focuses on various frequent pattern mining techniques, their challenges involved in static as well as stream data environment. Analysis: Information, the most precious asset is most of the times hidden inside heaps of raw data. It is required to be polished to retrieve information, converted into a form which can be analyzed for making decisions. Many research papers were read for the understanding of various techniques that mine frequent item sets either in static or stream data environments. Findings: This paper summarizes all the popular techniques available with us for frequent item set mining and provides some suggestions for optimizing them. The issues for static mining just take into account the time and space complexities but stream data mining is much more complex and challenging as compared to static. The problems like concept drifting, nature of data, its processing model, inadequacy, size, its retention in warehouses etc. are also required to be taken into account while working with real time data. Improvements: The algorithms for stream data mining should be incremental and resource adaptive in nature so that they can handle the change and adjust their processing parameters according to the availability of resources respectively. The available resources which will be very helpful in shared environments (where resources are shared by multiple processes).

Keywords

Data Mining Techniques, Frequent Patterns, Issues in Data Mining, Static Data Mining, Stream Data Mining.

Full Text:

 |  (PDF views: 217)

References


  • Han J, Kamber M, Pei J. Mining frequent patterns, associations, and correlations. Data Mining: Concepts and Techniques. 2nd(edn)., Morgan Kaufmann Publishers Inc.: San Francisco; 2006. p. 227–48.
  • Zarrouk M, Gouider MS. Frequent patterns mining in time-sensitive data stream. International Journal of Computer Science Issues. 2012 Jul; 9(4):117–24.
  • Aggarwal CC. Data streams: Models and algorithms. 1st(edn)., Springer US: US. 2007;1–7:61–100.
  • Gruenwald JN. Research issues in data stream association rule mining. ACM SIGMOD Record. 2006 Mar; 35(1):14–19.
  • Zhu Y, Shasha D. StatStream: Statistical monitoring of thousands of data streams in real time. ACM SIGMOD Record. 2005 Jun; 34(2):358–69.
  • Krempl G, Zliobaite I, Brzezinski D, Hullermeier E, Last M, Lemaire V, Noack T, Shaker A, Sievi S, Spiliopoulou M, Stefanowski J. Open challenges for data stream mining research. ACM SIGKDD Explorations Newsletter - Special issue on big data. 2014 Jun; 16(1):1–10.
  • Kohavi R, Mason L, Parekh R, Zheng Z. Lessons and challenges from mining retail e-commerce data. Kluwer Academic Publishers .2004 Oct; 57(1):83–113.
  • Yassir A, Nayak S. Issues in data mining and information retrieval. International Journal of Computer Science and Communication Networks. 2012 Mar; 2(1):93–8.
  • Kanth MR, Loshma G. Parallel multithreaded apriori algorithm for vertical association rule mining. International Journal of Advanced Research in Computer and Communication Engineering. 2013 Dec; 2(12):4729–35.
  • Dandu S, Deekshatulu BL, Chandra P. Improved algorithm for frequent item sets mining based on apriori and FP-tree. Global Journal of Computer Science and Technology Software and Data Engineering. 2013; 13(2):13–16.
  • Maolegi M, Arkok B. An improved apriori algorithm for association rules. International Journal on Natural Language Computing. 2014 Feb; 3(1):21–9.
  • Yabing J. Research of an improved apriori algorithm in data mining association rules. International Journal of Computer and Communication Engineering. 2013 Jan; 2(1):25–7.
  • Chandrika J, Kumar KRA. Frequent itemset mining in transactional data streams on quality control and resource adaptation. International Journal of Data Mining & Knowledge Management Process. 2012 Nov, 2(6):1–12.
  • Li HF, Ho CC, Shan MK, Lee SY. Efficient maintenance and mining of frequent itemsets over online data streams with a sliding window. IEEE International Conference on Systems, Man, and Cybernetics. 2006 Oct; 3:2672–7.
  • Li Y, Chang C, Yeh J. An algorithm for mining association rules with weighted minimum supports. 1st(edn)., Artificial Intelligence Applications and Innovations. Springer US: Beijing; 2005. p. 291–300.
  • Feng W, Quanyuan W, Yan Z, Xin J. Mining frequent patterns in data stream over sliding windows. Proceedings of International Conference On Computational Intelligence and Software Engineering. 2009 Dec; p. 1–4.
  • Leung CKS, Hao B. Mining of frequent itemsets from uncertain data. Proceedings of IEEE 25th International Conference on Data Engineering; Shanghai; 2009 Mar. p. 1663–70.
  • Gaber MM, Krishnaswamy S, Zaslavsky A. Cost-efficient mining techniques for data streams. Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation,Australia; 2004 Jan. p. 109–14.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.