An advanced frequent closed sequences using BIDE

  • Authors

    • K Vineela
    • M V.B.T. Santhi
    • N V.V. Gowtham Srujan
    • V Ashok
    2018-04-18
    https://doi.org/10.14419/ijet.v7i2.20.11764
  • Mining, frequent closed sequences, Bi-directional extension.
  • According to the past reasearches which produced few argumented stating that the frequent mining algorithm should only be closed but not frequent, as it not only results in compact but also complete results, and also in greater effectiveness. Most of the previous algorithms have mainly provided a direct test strategy to detect. In this article, we provide an Advanced BIDE, which is an effective algorithm used for processing query methods frequently closed. BI-Directional extension algorithm is better in pruning or filtering the search space when compared to any other algorithm. It is related to the calculation of frequent samples of search engines by parent-child relationships. An experimental study based on a variety of real historical data demonstrates the effectiveness and measurability of A-BIDE on the known alternatives of the past. It can also be scaled in terms of size of a query.

     


  • References

    1. [1] Chen Q, Lim A & Ong KW, “D (k)-index: An adaptive structural summary for graph-structured dataâ€, Proceedings of the ACM SIGMOD international conference on Management of data, (2003), pp.134-144.

      [2] Kaushik R, Shenoy P, Bohannon P & Gudes E, “Exploiting local similarity for indexing paths in graph-structured dataâ€, Proceedings. 18th International Conference on Data Engineering, (2002), pp.129-140

      [3] Milo T & Suciu D, “Index structures for path expressionsâ€, Proc. the 7th Int. Conf. Database Theory, Jerusalem, Israel, (1999), pp.277–295.

      [4] Yang LH, Lee ML & Hsu W, “Efficient Mining of XML Query Patterns for Cachingâ€, Proceedings VLDB Conference, (2003), pp. 69-80.

      [5] Ren JD, Yang J & Li Y, “Mining weighted closed sequential patterns in large databasesâ€, Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Vol.5, (2008), pp.640-644.

      [6] Dehaspe L, Toivonen H & King RD, “Finding Frequent Substructures in Chemical Compoundsâ€, KDD, Vol.98, (1998).

      [7] Bettini C, Wang XS & Jajodia S, “Mining temporal relationships with multiple granularities in time sequencesâ€, IEEE Data Eng. Bull., Vol.21, No.1, (1998), pp.32-38.

      [8] Han J, Pei J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U & Hsu M, “Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growthâ€, Proceedings of the 17th international conference on data engineering, (2001), pp.215-224.

      [9] Feng J, Qian Q, Wang J & Zhou L, “Exploit sequencing to accelerate hot XML query pattern miningâ€, Proceedings of the ACM symposium on Applied computing, (2006), pp.517-524.

      [10] Qian Q, Feng J, Wang J & Zhou L, “Exploit sequencing to accelerate XML twig query answeringâ€, International Conference on Database Systems for Advanced Applications, (2006), pp.279-294.

      [11] Wang J & Han J, “BIDE: Efficient mining of frequent closed sequencesâ€, Proc. the 20th Int. Conf. Data Engineering, Boston, (2004), pp.79–90.

      [12] Kuramochi M & Karypis G, “Frequent subgraph discoveryâ€, Proc. the 1st IEEE Int. Conf. Data Mining, San Jose, CA, USA, (2001), pp.313–320.

      [13] Agrawal R & Srikant R, “Fast algorithms for mining association rulesâ€, Proc. the 20th Int. Conf. Very Large Data Bases, Santiago de Chile, Chile, (1994), pp.487–499.

      [14] Zaki M, “Efficiently mining frequent trees in a forestâ€, Proc. the 8th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, (2002), pp.71–80.

      [15] Asai T, Abe K, Kawasoe S, Arimura H, Satamoto H & Arikawa S, “Efficient Substructure Discovery from Large Semi-structured Dataâ€, 2nd SIAM Int’l Conference on Data Mining, (2002).

      [16] Termier A, Rousset MC & Sebag M, “Treefinder: a first step towards xml data miningâ€, Proceedings. IEEE International Conference on Data Mining, (2002), pp.450-457.

      [17] Han J, Pei J, Mortazavi-Asl B, Chen Q, Dayal U & Hsu MC, “FreeSpan: frequent pattern-projected sequential pattern miningâ€, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, (2000), pp.355-359.

      [18] Masseglia F, Cathala F & Poncelet P, “The PSP approach for mining sequential patternsâ€, European Symposium on Principles of Data Mining and Knowledge Discovery,(1998), pp.176-184.

      [19] Srikant R & Agrawal R, “Mining sequential patterns: Generalizations and performance improvementsâ€, Proc. the 5th Int. Conf. Extending Database Technology, Avignon, France, (1996), pp.3–17.

      [20] Ozden B, Ramaswamy S & Silberschatz A, “Cyclic association rulesâ€, Proceedings 14th International Conference on Data Engineering, (1998), pp.412-421.

      [21] Han J, Dong G & Yin Y, “Efficient mining of partial periodic patterns in time series databaseâ€, Proceedings 15th International Conference on Data Engineering, (1999), pp.106-115.

      [22] Yang J, Wang W, Yu PS & Han J, “Mining long sequential patterns in a noisy environmentâ€, Proceedings of the ACM SIGMOD international conference on Management of data, (2002), pp.406-417.

      [23] Chi Y, Xia Y, Yang Y & Muntz RR, “Mining closed and maximal frequent subtrees from databases of labeled rooted treesâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.2,(2005), pp.190-202.

      [24] Raw PR & Moon B, “PRIX: Indexing and querying XML using Prufer sequences†Proc. the 20th Int. Conf. Data Engineering, Boston, MA, USA, (2004), pp.288–300.

      [25] Picciotto S, “How to encode a tree [Dissertation]â€, University of California, San Diego, USA, (1999).

  • Downloads

  • How to Cite

    Vineela, K., V.B.T. Santhi, M., V.V. Gowtham Srujan, N., & Ashok, V. (2018). An advanced frequent closed sequences using BIDE. International Journal of Engineering & Technology, 7(2.20), 101-104. https://doi.org/10.14419/ijet.v7i2.20.11764