Abstract
In recent years, the procedure of manufacturing has become more and more complex. In order to meet high expectation on quality target, quick identification of root cause that makes defects is an essential issue. In this paper, we will refer to a typical algorithm of mining association rules and propose a novel interestingness measurement to provide an effective and accurate solution. First, the manufacturing defect detection problem of analyzing the correlation between combinations of machines and the result of defect is defined. Then, we propose an integrated processing procedure RMI (Root cause Machine Identifier) to discover the root cause in this problem. Finally, the results of experiments show the accuracy and efficiency of RMI are both well with real manufacturing cases.
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
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large database. In: Proc. ACM SIGMOD Conference, pp. 207–216 (1993)
Agrawl, R., Srikant, R.: Fast Algorithm for Mining Association rules. In: Proc. ACM VLDB Conference, pp. 487–499 (1994)
Brin, S., Motwani, R., Silverstein, C.: Beyond Market Basket: Generalizing Association Rules to Correlations. In: Proceeding of ACM SIGMOD Conference, pp. 265–276 (1997)
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Data. In: Proc. ACM SIGMOD Conference, pp. 255–264 (1997)
Catledge, L.D., Pitkow, J.E.: Characterizing Browsing Strategies in the World Wide Web. In: Proc. Third WWW Conference (April 1995)
Cheeseman, P., Stutz, J.: Bayesian Classification (AutoClass): Theory and Results. In: Fayyad, U.M., Smyth, P.G., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 153–180. AAAI/MIT Press (1996)
Chen, M.S., Han, J., Yu, P.S.: Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering 8(6) (1996)
Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Approach. In: Proc. IEEE International Conference on Data Engineering, pp. 106–114 (1996)
Ester, M., Kriegel, H.O., Xu, X.: Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification. In: Proc. Fourth International Symp. Large Spatial Databases, pp. 67–82
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast Subsequence Matching in Time-Series Databases. In: Proc. ACM SIGMOD Conference, pp. 419–429 (1994)
Freitas, A.A.: On Rule Interestingness Measures. Knowledge-Based System, 309–315 (1999)
Gardner, M., Bieker, J.: Mining Solves Tough Semiconductor Manufacturing Problems. In: Proc. ACM KDD Conference (2000)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
Hilderman, R.J., Hamilton, H.J.: Heuristic Measures of Interestingness. Principles of Data Mining and Knowledge Discovery, 232–241 (1999)
Kaufuman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, Chichester (1990)
Mieno, F., Santo, T., Shibuya, Y., Odagiri, K., Tsuda, H., Take, R.: Yield Improvement Using Data Mining System. In: Proc. IEEE Semiconductor Manufacturing Conference (1999)
Ng, R., Han, J.: Efficient and Effective Clustering Method for Spatial Data Mining. In: Proc. ACM VLDB Conference, pp. 144–155 (1994)
Park, J.S., Chen, M.S., Yu, P.S.: An effective hash-based algorithm for mining association rules. In: Proc. ACM SIGMOD Conference, pp. 175–186 (1995)
Park, J.S., Chen, M.S., Yu, P.S.: Mining Association Rules with Adjustable Accuracy. IBM Research Report (1995)
Park, J.S., Chen, M.S., Yu, P.S.: Efficient Parallel Data Mining for Association Rules. In: Proc. ACM CIKM Conference, pp. 175–186 (1995)
Piatestsky-Shaprioc, G.: Discovery, Analysis and Presentation of Strong Rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 229–247. AAAI Press, Menlo Park (1991)
Quinlanc, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Press, San Francisco (1993)
Raghavan, V.: Application of Decision Trees for Integrated Circuit Yield Improvement. In: Proc. IEEE/SEMI Advanced Semiconductor Manufacturing Conference & Workshop (2002)
Silberschatz, A., Tuzhilin, A.: What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Transaction on Knowledge and Data Engineering (1996)
Tan, P.N., Kumar, V.: Interestingness Measures for Association Patterns: A Perspective. In: Proc. KDD 2000 Workshop on Postprocessing in Machine Learning and Data Mining (2000)
Weiss, S.M., Kulikowski, C.A.: Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets. In: Machine Learning and Expert Systems, Morgan Kaufman Press, San Francisco (1991)
Wur, S.Y., Leu, Y.: An Effective Boolean Algorithm for Mining Association Rules in Large Databases. In: Proc. International Conference on Database Systems for Advanced Applications (1999)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An Efficient Data Clustering Method for Very Large Databases. In: Proc. ACM SIGMOD Conference, pp. 103–114 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, WC., Tseng, SS., Wang, CY. (2004). A Novel Manufacturing Defect Detection Method Using Data Mining Approach. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-24677-0_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
Online ISBN: 978-3-540-24677-0
eBook Packages: Springer Book Archive