Abstract
This paper proposed a model based on hierarchical Clustering and Association Rule, which is intended for decision support system. The proposed system is intended to address the shortcomings of other data mining tools on the processing time and efficiency when generating association rules. This study will determine the data structures by implementing the cluster analysis which is integrated in the proposed architecture for data mining process and calculate for associations based on clustered data. The results were obtained using the proposed system as integrated approach and were rendered on the synthetic data. Although, our implementation uses heuristic approach, the experiment shows that the proposed system generated good and understandable association rules, which could be practically explained and use for the decision support purposes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Han, J., Kamber, M.: Data Mining Concepts & Techniques. Morgan Kaufmann, USA (2001)
Pressman, R.: Software Engineering: a practitioner’s approach, 6th edn. McGraw-Hill, USA (2005)
Hellerstein, J.L., Ma, S., Perng, C.S.: Discovering actionable patterns in event data. IBM Systems Journal 41(3) (2002)
Multi-Dimensional Constrained Gradient Mining, ftp://fas.sfu.ca/pub/cs/theses/2001/JoyceManWingLamMSc.pdf
Chen, B., Haas, P., Scheuermann, P.: A new two-phase sampling based algorithm for discovering association rules. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2002)
Margaritis, D., Faloutsos, C., Thrun, S.: NetCube: A Scalable Tool for Fast Data Mining and Compression. In: 27th Conference on Very Large Databases (VLDB), Roma, Italy (September 2001)
Han, E.H., Karypis, G., Kumar, V., Mobasher, B.: Clustering in a high-dimensional space using hypergraph models (1998), http://www.informatik.uni-siegen.de/~galeas/papers/general/Clustering_in_a_High-Dimensional_Space_Using_Hypergraphs_Models_%28Han1997b%29.pdf
Cluster Analysis defined, http://www.clustan.com/what_is_cluster_analysis.html
Determining the Number of Clusters, http://cgm.cs.mcgill.ca/soss/cs644/projects/siourbas/cluster.html#kmeans
Using Hierarchical Clustering in XLMiner, http://www.resample.com/xlminer/help/HClst/HClst_intro.htm
Ertz, L., Steinbach, M., Kumar, V.: Finding Topics in Collections of Documents: A Shared Nearest Neighbor Approach. In: Text Mine 2001, Workshop on Text Mining, First SIAM International Conference on Data Mining, Chicago, IL (2001)
Hruschka, E.R., Hruschka Jr., E.R., Ebecken, N.F.F.: A Nearest-Neighbor Method as a Data Preparation Tool for a Clustering Genetic Algorithm. In: SBBD, pp. 319–327 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gerardo, B.D., Byun, YC., Tanguilig, B. (2011). Hierarchical Clustering and Association Rule Discovery Process for Efficient Decision Support System. In: Kim, Th., et al. Communication and Networking. FGCN 2011. Communications in Computer and Information Science, vol 266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27201-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-27201-1_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27200-4
Online ISBN: 978-3-642-27201-1
eBook Packages: Computer ScienceComputer Science (R0)