Abstract
Using fuzzy c-means as the data-mining tool, this study evaluates the effectiveness of sampling methods in producing the knowledge of interest. The effectiveness is shown in terms of the representative-ness of sampling data and both the accuracy and errors of sampled data sets when subjected to the fuzzy clustering algorithm. Two population data in the weld inspection domain were used for the evaluation. Based on the results obtained, a number of observations are made.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agarwal, R., Gehrke, J., Gunopulos, D., and Raghavan, P., “Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications,” SIGMOD ‘88, Seattle, WA, 94–105, 1998.
Ball, G. H. and Hall, D. J., ISODATA, an iterative method of multivariate analysis and pattern recognition, Behavior Science, 153, 1967.
Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithms ( Plenum Press, New York and London, 1987 ).
Chen, M.-S., Han, J., and Yu, P. S., “Data Mining: An Overview from a Database Perspective,” IEEE Transactions on Knowledge and Data Engineering, 8 (6), 866–883, 1996.
Dunn, J. C., A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, J. Cybernet., 3, 1974, 32–57.
Duran, B. S. and Odell, P. L., Cluster Analysis: a Survey, Volume 100 of Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, 1974.
Guha, S., Rastogi, R., and Shim, K., “CURE: An Efficient Clustering Algorithm for Large Databases,” SIGMOD ‘88, Seattle, WA, 73–84, 1998.
Kohavi, R., Sommerfield, D., and Dougherty, J., Data Mining Using MLC++: A Machining Learning Library in C++, http://robotics.stanford.edu/—ronnyk.
Krishnapuram, R. and Keller, J. M.. “A Possibilistic Approach to Clustering,” IEEE Trans. on Fuzzy Systems, 1 (2), 1993, 98–110.
Liao, T. W., Li, D.-M., and Li, Y.-M., “Extraction of Welds from Radiographic Images Using Fuzzy Classifiers,” Information Sciences, 126, 21–42, 2000.
Liao, T. W., Li, D.-M., and Li, Y.-M., “Detection of Welding Flaws from Radiographic Images with Fuzzy Clustering Methods”, Fuzzy Sets and Systems, 108 (2), 145–158, 1999.
Loslever, P., Lepoutre, F. X., Kebab, A., and Sayarh, H., “Descriptive multidimensional statistical methods for analyzing signals in a multifactorial biomedical database,” Med. & Biol. Eng. & Compt., 34, 13–20, 1996.
Ng, R. T. and Han, J., “Efficient and Effective Clustering Methods for Spatial Data Mining,” in Proc. of the VLDB Conference, Santiago, Chile, 144–155, 1994.
Quinlan, J. R., C4.5: Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann, 1993.
Rana, O. F. and Fisk, D., “A Distributed Framework for Parallel Data Mining Using HPJava,” BT Technology Journal, 17 (3), 146–154, 1999.
Reinartz, T., Focusing Solutions for Data Mining, Springer, 1999.
Zhang, T., Ramakrishnan, R., and Livny, M., “BIRCH: An Efficient Data Clustering Method for Very Large Databases, ” in Proc. of the ACM SIGMOD Conference on Management of Data, Montreal, Canada, June 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Josien, K., Wang, G., Liao, T.W., Triantaphyllou, E., Liu, M.C. (2001). An Evaluation of Sampling Methods for Data Mining with Fuzzy C-Means. In: Braha, D. (eds) Data Mining for Design and Manufacturing. Massive Computing, vol 3. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-4911-3_15
Download citation
DOI: https://doi.org/10.1007/978-1-4757-4911-3_15
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-5205-9
Online ISBN: 978-1-4757-4911-3
eBook Packages: Springer Book Archive