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Attribute -TID Method for Discovering Sequence of Attributes

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Data Engineering and Management (ICDEM 2010)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6411))

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Abstract

The abstraction based algorithms read databases in sequential order and then construct abstraction of the database in memory. Given any database with n attributes, it is possible to read the same in n! ways. These different n! ways lead to abstractions of different sizes. In this paper, for a given a set of transactions D, we find the sequence or order of the attributes in which the database is read, a representation which is compact than PC-tree, can be obtained in the memory.

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References

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Massive Databases. In: Proceedings of the ACM-SIGMOD International Conference on Management of Data, Washington, D.C, pp. 207–216 (1993)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for Mining association rules. In: Proceedings of the 20th VLDB Conference, pp. 487–499 (1994)

    Google Scholar 

  3. Ananthanarayana, V.S., Subramanian, D.K., Murty, M.N.: Scalable, Distributed and Dynamic Mining of Association Rules. In: Valero, M., Prasanna, V.K., Vajapeyam, S. (eds.) HiPC 2000. LNCS, vol. 1970, pp. 559–566. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  4. Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proceedings of ACM-SIGMOD International Conference Management of Data, Dallas, TX, pp. 1–12 (2000)

    Google Scholar 

  5. Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Franscisco (2008)

    MATH  Google Scholar 

  6. Han, J., Jian, P., Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent Pattern Tree Approach. Data Mining and Knowledge Discovery, 53–87 (2004)

    Google Scholar 

  7. Kumar, R., Kumar, P., Ananthanarayana, V.S.: Finding the Boundaries of Attributes Domains for Quantitative Association Rules using Abstraction - A Dynamic Approach. In: Proceedings of the 7th WSEAS International Conference on Applied Computer Science, Venice, Italy, pp. 52–58 (2007)

    Google Scholar 

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Kumar, P., Ananthanarayana, V.S. (2012). Attribute -TID Method for Discovering Sequence of Attributes. In: Kannan, R., Andres, F. (eds) Data Engineering and Management. ICDEM 2010. Lecture Notes in Computer Science, vol 6411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27872-3_49

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  • DOI: https://doi.org/10.1007/978-3-642-27872-3_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27871-6

  • Online ISBN: 978-3-642-27872-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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