The K-Means Clustering Architecture in the Multi-stage Data Mining Process

In this paper, we used software engineering principles for the development of models and proposed the K-Means clustering architecture implemented on the multi-stage data mining process. We developed a modified architecture and expanded it by showing refinements on every process of the clustering and knowledge discovery stages. We used the mentioned hierarchical clustering model to partition the data into smaller groups of attributes so that we would determine the data structure before applying the data mining tools. The experiment shows that the model using the clustering resulted to an isolated but imperative association rules based on clustered data, which in return could be practically explained for decision making purposes. Shorter processing time had been observed in computing for smaller clusters implying faster and ideal processing period than dealing with the entire dataset.

Authors and Affiliations

1. School of Electronic and Information Engineering, Kunsan National University, 68 Miryong-dong, Kunsan, Chonbuk, 573-701, South Korea

   Bobby D. Gerardo, Jae-Wan Lee & Yeon-Sung Choi

2. School of Electronic and Information Engineering, Chonbuk National University, 664-14 Deokjin-dong, Jeonju, Chonbuk, 561-756, South Korea

   Malrey Lee

Editors and Affiliations

1. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

   Osvaldo Gervasi

2. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

   Marina L. Gavrilova

3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

   Vipin Kumar

4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, P.O. Box, I-06123, Perugia, Italy

   Antonio Laganà

5. Institute of High Performance Computing, IHCP, 1 Science Park Road, 01-01 The Capricorn, Singapore Science Park II, 117528, Singapore

   Heow Pueh Lee

6. School of Computing, Soongsil University, Seoul, Korea

   Youngsong Mun

7. Clayton School of IT, Monash University, 3800, Clayton, Australia

   David Taniar

8. OptimaNumerics Ltd, P.O. Box, Belfast, United Kingdom

   Chih Jeng Kenneth Tan

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© 2005 Springer-Verlag Berlin Heidelberg

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