Emancipation of FP Growth Algorithm using Association Rules on Spatial Data Sets

Authors(2) :-Sudheer Kumar Muppalla, Raveendra Reddy Enumula

During the process of mining frequent item sets, when minimum support is little, the production of candidate sets is a kind of time-consuming and frequent operation in the mining algorithm. The FP growth algorithm does not need to produce the candidate sets, the database which provides the frequent item set is compressed to a frequent pattern tree (or FP tree), and frequent item set is mining by using of FP tree. These algorithms considered as efficient because of their compact structure and also for less generation of candidates itemsets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (FP-Growth, COFI-Tree, CT-PRO) based upon the FP- Tree like structure for mining the frequent item sets along with their capabilities and comparisons. Data mining implementation on spatial data to generate rules and patterns using Frequent Pattern (FP)-Growth algorithm is the major concern of this research study. We presented in this paper how data mining can apply on spatial data.

Spatial Data Mining, Association Mining, FP-Growth Algorithm, Frequent data sets

  1. R.Agrawal, R.Srikant, "Fast algorithms for mining association rules", Proceedings of the 20th Very Large DataBases Conference (VLDB’94), Santiago de Chile, Chile, 1994, pp. 487-499.
  2. J.Han, J.Pei and Y.Yin., "Mining frequent patterns without candidate Generation", in: Proceeding of ACM SIGMOD International Conference Management of Data, 2000, pp.1-12 .
  3. Jiawei Han, M.Kamber, "Data Mining-Concepts and Techniques", Morgan Kanufmann Publishers, Sam Francisco, 2009.
  4. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. 1993 ACMSIGMOD Int. Conf. Management of Data, pages 207–216, Washington, D.C., May 1993.
  5. M.-L. Antonie and O. R. Za¨?ane. Text document categorization by term association. In IEEE International Conference on Data Mining, pages 19–26, December 2002.
  6. J. Hipp, U. Guntzer, and G. Nakaeizadeh. Algorithms for association rule mining - a general survey and comparison. ACM SIGKDD Explorations, 2(1):58–64, June 2000.
  7. M. El-Hajj and O. R. Za¨?ane. Inverted matrix: Efficient discovery of frequent items in large datasets in the context of interactive mining. In Proc. 2003 Int’l Conf. on Data Mining and Knowledge Discovery (ACM SIGKDD), August 2003.
  8. M. El-Hajj and O. R. Za¨?ane: COFI-tree Mining:A New Approach to Pattern Growth with Reduced Candidacy Generation. Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, 19 December 2003, Melbourne, Florida, USA, CEUR Workshop Proceedings, vol. 90 (2003).
  9. R. P. Gopalan and Y. G. Sucahyo, "High Performance Frequent Pattern Extraction using Compressed FPTrees", Proceedings of SIAM International Workshop on High Performance and Distributed Mining (HPDM),Orlando, USA, 2004.
  10. FIMI, "FIMI Repository", 2004, http://fimi.cs.helsinki.fi, last accessed at 20/04/2011.

Publication Details

Published in : Volume 3 | Issue 5 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 604-608
Manuscript Number : IJSRSET1734102
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Sudheer Kumar Muppalla, Raveendra Reddy Enumula, " Emancipation of FP Growth Algorithm using Association Rules on Spatial Data Sets, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 5, pp.604-608, July-August-2017. Citation Detection and Elimination     |     
Journal URL : https://ijsrset.com/IJSRSET1734102

Article Preview