CFP last date
20 December 2024
Reseach Article

Optimization of Spatial Association Rule Mining using Hybrid Evolutionary Algorithm

by J. Arunadevi, V. Rajamani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 19
Year of Publication: 2010
Authors: J. Arunadevi, V. Rajamani
10.5120/397-592

J. Arunadevi, V. Rajamani . Optimization of Spatial Association Rule Mining using Hybrid Evolutionary Algorithm. International Journal of Computer Applications. 1, 19 ( February 2010), 86-89. DOI=10.5120/397-592

@article{ 10.5120/397-592,
author = { J. Arunadevi, V. Rajamani },
title = { Optimization of Spatial Association Rule Mining using Hybrid Evolutionary Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 19 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 86-89 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number19/397-592/ },
doi = { 10.5120/397-592 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:43:08.946658+05:30
%A J. Arunadevi
%A V. Rajamani
%T Optimization of Spatial Association Rule Mining using Hybrid Evolutionary Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 19
%P 86-89
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Spatial data refer to any data about objects that occupy real physical space. Attributes within spatial databases usually include spatial information. Spatial data refers to the numerical or categorical values of a function at different spatial locations. Spatial metadata refers to the descriptions of the spatial configuration. Application of classical association rule mining concepts to spatial databases is promising but very challenging. Spatial Association Rule Mining requires new approaches compared to classical association rule mining. Spatial data consists of dependent events compared to transactional data which consist of independent transactions. It is more difficult to classify a discovered spatial association rule as interesting. Instead of much generalized rule more specific rule discovery needs further research.

References
  1. Margaret H.Dunham and S.Sridhar. “Data Mining Introductory and Advanced Topics”, Pearson Education, 2006.
  2. Alex A. Freitas, “A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery” Postgraduate Program in Computer Science, Pontificia Universidade Catolica do Parana Rua Imaculada Conceicao, 1155. Curitiba - PR. 80215-901. Brazil.
  3. Dehuri, S., Jagadev, A. K., Ghosh A. And Mall R. 2006. Multi-objective Genetic Algorithm for Association Rule Mining Using a Homogeneous Dedicated Cluster of Workstations. American Journal of Applied Sciences 3 (11): 2086-2095, 2006 ISSN 1546-9239.
  4. Peter P. Wakabi-Waiswa and Venansius Baryamureeba. Extraction of Interesting Association Rules Using Genetic Algorithms. International Journal of Computing and ICT Research, Vol. 2, No. 1, pp. 26 – 33. http:www.ijcir.org/volume2-number1/article4.pdf.
  5. www.dsi.unive.it/~dm/ssd95.pdf
  6. Freitas, A.A., 2003. A survey of evolutionary algorithms for data mining and knowledge discovery. In: A. Ghosh, S. Tsutsui (Eds.), Advances in Evolutionary Computing, Springer Verlag, New York, pp: 819–845.
  7. Dehuri, S. and R. Mall, 2004. Mining predictive and comprehensible rules using a multi-objective genetic algorithm. Advance Computing and Communication (ADCOM), India.
  8. “Association Rule Analysis of Spatial Data Mining Based on Matlab”, Workshop on Knowledge Discovery and Data Mining , 2008 IEEE DOI 10.1109/WKDD.2008.21
  9. A . Colorni, M. Dorigo, and V. Maniezzo. Positive feedback as a search strategy. Technical Report No. 91-016, Politecnico di Milano, Italy,1991.
  10. A. Colorni, M. Dorigo, and V. Maniezzo . The ant system: an autocatatlytic process. Technical Report No. 91- 016, Politecnico di Milano, Italy, 1991.
  11. R. Beckers, J.L. Deneubourg and S. Goss. Trails and Uturns in the selection of the shortest path by the ant lasius niger. Journal of Theoretical Biology, 159, 1992, pp. 397- 415.
  12. M. Dorigo. Optimization, Learning and Natural Algorithms. Ph.D. Thesis, Politecnico di Milano, Italy,1992
  13. S. Goss, S. Aron, J.L. Deneubourg and J.M. Pasteels (). Self-organized shortcuts in the argentine ant. Naturwissenschaften, 76, 1989, pp. 579-581.
  14. B. Holldobler and E.O. Wilson (1990). The Ants. Springer-Verlag: Berlin.
Index Terms

Computer Science
Information Sciences

Keywords

Spatial Association Rule Mining Evolutionary Optimization Algorithms Genetic Algorithms ACO