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Data Stream Mining: A Review

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Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 150))

Abstract:

In the data stream model the data arrives at high speed so that the algorithms used for mining the data streams must process them in a very strict constraints of space and time. This raises new issues that need to be considered when developing association rule mining algorithms for data streams. So it is important to study the existing stream mining algorithms to open up the challenges and the research scope for the new researchers. In this paper we are briefly discussing the different issues and challenges in the data stream mining.

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References

  1. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Proceedings of the SIGMOD

    Google Scholar 

  2. Garofalakis M, Gehrke J, Rastogi R (2002) Querying and mining data streams: you only get one look. In: Tutorial of SIGMOD

    Google Scholar 

  3. Li H-F, Lee S-Y, Shan M-K (2005) Online mining (recently) maximal frequent itemsets over data streams. In: Proceedings of the 15th international workshop on research issues in data engineering. Stream data mining and applications (RIDE-SDMA’05)

    Google Scholar 

  4. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: Proceedings of ICDT

    Google Scholar 

  5. Chi Y, Wang H, Yu PS, Muntz RR (2006) Catch the moment: maintaining closed frequent itemsets over a data stream sliding window. KAIS 10(3):265–294

    Google Scholar 

  6. Lee D, Lee W (2005) Finding maximal frequent itemsets over online data streams adaptively. In: Proceedings of ICDM

    Google Scholar 

  7. Calders T, Goethals B (2002) Mining all nonderivable frequent itemsets. In: Proceedings of PKDD

    Google Scholar 

  8. Xin D, Han J, Yan X, Cheng H (2005) Mining compressed frequent-pattern sets. In: Proceedings of VLDB

    Google Scholar 

  9. Bonchi F, Lucchese C (2005) On condensed representations of constrained frequent patterns. KAIS 9(2):180–201

    Google Scholar 

  10. Zaki M, Parthasarathy S, Li W, Ogihara M (1997) Evaluation of sampling for data mining of association rules. In: Proceedings of RIDE

    Google Scholar 

  11. Srivastava U, Widom J (2004) Memory-limited execution of windowed stream joins. In: Proceedings of VLDB

    Google Scholar 

  12. Otey ME, Wang C, Parthasarathy S, Veloso A, Meira W Jr (2003) Mining frequent itemsets in distributed and dynamic databases. In: IEEE international conference on data mining

    Google Scholar 

  13. Otey ME, Parthasarathy S, Wang, Veloso A, Meira W Jr (2004) Parallel and distributed methods for incremental frequent itemset mining. IEEE Trans Sys Man Cybernet

    Google Scholar 

  14. Assaf S, Wolff R, Trock D (2003) Distributed algorithm for mining association rules. IEEE international conference on data mining

    Google Scholar 

  15. Richard M.K, Shenker S (2003) A simple algorithm for finding frequent elements in streams and bags. ACM Trans Database Sys

    Google Scholar 

  16. Li Y, Sanver M (2004) Mining short association rules with one database scan. In: International conference on information and knowledge engineering

    Google Scholar 

  17. Manku GS and Motwani R (2002) Approximate frequency counts over data streams. In: Proceedings of the VLDB

    Google Scholar 

  18. Li H, Lee S, Shan M (2004) An efficient algorithm for mining frequent itemsets over the entire history of data streams. In: Proceedings of the first international workshop on knowledge discovery in data streams

    Google Scholar 

  19. Chris G, Han J, Pei J, Yan X, Yu PS (2003) Mining frequent patterns in data streams at multiple time granularities. Data mining: next Generation challenges and future directions, AAAI/MIT

    Google Scholar 

  20. Jin R, Agrawal G (2005) An algorithm for in-core frequent itemset mining on streaming data. In: Proceedings of ICDM

    Google Scholar 

  21. Chi Y, Wang H, Yu PS, Muntz RR (2004) Moment: maintaining closed frequent itemsets over a stream sliding window. In: Proceedings of ICDM

    Google Scholar 

  22. David WC, Han J, Ng VT, Wong CY (1996) Maintenance of discovered association rules in large databases: an incremental updating technique. In: IEEE international conference on data mining

    Google Scholar 

  23. Yu J, Chong Z, Lu H, Zhou A (2004) False positive or false negative: mining frequent itemsets from high speed transactional data streams. In: Proceedings of VLDB

    Google Scholar 

  24. Haixun W, Fan W, Yu PS, Han J (2003) Mining concept-drifting data streams using ensemble classifiers. In: ACM SIGKDD International conference on knowledge discovery and data mining

    Google Scholar 

  25. Chang JH, Lee WS (2004) A sliding window method for finding recently frequent itemsets over online data streams. J Inform Sci Eng 20(4)

    Google Scholar 

  26. Li L (2009) Mining frequent itemsets over data streams using efficient window sliding techniques. Sci Direct Expert Sys Appl 36:1466–1477

    Article  Google Scholar 

  27. Chih-Hsiang L, Chiu D-Y, Wu Y-H, Chen ALP (2005) Mining frequent itemsets from data streams with a time-sensitive sliding window. In: SIAM International conference on data mining

    Google Scholar 

  28. Pinto H, Han J, Pei J, Wang K, Chen Q, Dayal U (2001) Multi- dimensional sequential pattern mining. In: International conference on information and knowledge management

    Google Scholar 

  29. Parthasarathy S, Zaki MJ, Ogihara M, Dwarkadas S (1999) Incremental and interactive sequence mining. In: International conference on information and knowledge management

    Google Scholar 

  30. Adriano V, Otey M, Meira P Jr (2003) Parallel and distributed frequent itemset mining on dynamic datasets. In: International conference on high performance computing

    Google Scholar 

  31. Ghoting A, Parthasarathy S (2004) Facilitating interactive distributed data stream processing and mining. In: IEEE international symposium on parallel and distributed processing systems

    Google Scholar 

  32. Chang JH, Lee WS (2003) Finding recent frequent itemsets adaptively over online data streams. In: Proceedings of the KDD

    Google Scholar 

  33. Graham C, Muthukrishnan S (2005) What’s hot and What’s not: tracking most frequent items dynamically. ACM Trans Database Sys

    Google Scholar 

  34. Qingguo Z, Xu K, Ma S (2003) When to update the sequential patterns of stream data. In: Pacific-Asia conference on knowledge discovery and data mining

    Google Scholar 

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Author information

Authors and Affiliations

  1. Associate Professor, Information Technology, Christian College of Engineering, Bhilai, C.G, India

    S Pramod

  2. IIIT-Allahabad, Allahabad, U.P, India

    O. P. Vyas

Authors
  1. S Pramod
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  2. O. P. Vyas
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Corresponding author

Correspondence to S Pramod .

Editor information

Editors and Affiliations

  1. Saintgits College of Engineering, Kottayam, India

    Vinu V. Das

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Pramod, S., Vyas, O.P. (2013). Data Stream Mining: A Review. In: Das, V. (eds) Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing. Lecture Notes in Electrical Engineering, vol 150. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3363-7_75

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  • DOI: https://doi.org/10.1007/978-1-4614-3363-7_75

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-3362-0

  • Online ISBN: 978-1-4614-3363-7

  • eBook Packages: EngineeringEngineering (R0)

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