A MANUFACTURING FAILURE ROOT CAUSE ANALYSIS IN IMBALANCE DATA SET USING PCA WEIGHTED ASSOCIATION RULE MINING
Keywords:ARM, root cause analysis, WARM, PCA
AbstractRoot cause analysis is key issue for manufacturing processes. It has been a very challenging problem due to the increasing level of complexity and huge number of operational aspects in manufacturing systems. Association rule mining (ARM) which aids in root cause analysis was introduced to extract interesting correlations, frequent patterns, associations or casual structures among items in the transactional database. Although ARM was proven outstanding in many application domains, not many researches were focusing on solving rare items problem in imbalance dataset. The existence of imbalanced dataset in manufacturing environment make the classical ARM fails to extract interesting pattern in an efficient way. Weighted association rule mining (WARM) overcomes the rare items problem by assigning weights to items. The goal of using weighted support is to make use of the weight in the mining process and priorities the selection of the selection of targeted itemsets according to their significance, rather than frequency alone. However, the development of a suitable weight assignment scheme has been an important issue. In this research, we proposed principal component analysis (PCA) to automate the weight in WARM. The result shows that PCA-WARM is capable in capturing pattern from the data of industrial process. These patterns are proven able to explain industrial failure.
D. Tohmatsu. 2012. The Future of Manufacturing: Opportunities to Drive Economic Growth.
R. Hausmann and C. Hidalgo. 2014. The Atlas of Economic Complexity: Mapping Paths to Prosperity. MIT Press.
M. James, S. Jeff, D. Richarcd, S. Gernot, R. Louis, M. Jan, R. Jaana, R. Charles, G. Katy, O. David, and R. Sreenivas. 2012. Manufacturing the Futureâ€¯: The Next Era of Global Growth and Innovation.
L. Rokach and D. Hutter. 2012. Automatic Discovery of the Root Causes for Quality Drift in High Dimensionality Manufacturing Processes. J. Intell. Manuf. 23(5): 1915-1930.
H. Yuniarto. 2012. The Shortcomings of Existing Root Cause Analysis Tools. Proc. World Congr. Eng. 3.
S. G. He, H. Zhen, A. Wang, and L. Li. 2009. Quality Improvement using Data Mining in Manufacturing Processes. In Data Mining and Knowledge Discovery in Real Life Applications, no. February, J. Ponce and A. Karahoca, Eds. I-Tech Education and Publishing. 436.
A. K. Choudhary, J. A. Harding, and M. K. Tiwari. 2008. Data Mining in Manufacturing: A Review Based on the Kind of Knowledge. J. Intell. Manuf. 20(5): 501-521.
U. Fayyad and R. Uthurusamy. 1996. Data Mining and Knowledge Discovery in Databases. Commun. ACM. 39(11): 24-26.
X. Z. Wang and C. McGreavy. 1998. Automatic Classification for Mining Process Operational Data. Ind. Eng. Chem. Res. 37(6): 2215â€“2222.
K. J. Cios, W. Pedrycz, R. W. Swiniarski, and L. Kurgan. 1996. Data Miningâ€¯: A knowledge Discovery Approach. Springer.
J. F. Halpin. Zero Defects: A New Dimension in Quality Assurance. Mc Graw-Hill.
J. J. Rooney and L. N. Van den Heuvel, 2004. Root Cause Analysis for Beginners. Qual. Prog. 45-53.
J. Soenjaya, W. Hsu, M. L. I. Lee, and T. Lee. 2005. Mining Wafer Fabrication: Framework and Challenges. In Next Generation of Data-Mining Application. M. M. Kantardzic and J. Zurada., Eds. New York: Wiley-IEEE Press. 17-40.
W. Keqin, T. Shurong, B. Eynard, L. Roucoules, and N. Matta. 2007. Review on Application of Data Mining in Product Design and Manufacturing. In Fuzzy Systems and Knowledge Discovery FSKD. 4: 613-618.
M. Polczynski and A. Kochanski. 2010. Knowledge Discovery and Analysis in Manufacturing. Qual. Eng. 22(3): 169-181.
W. J. Frawley, G. Piatetsky-shapiro, and C. J. Matheus. 1992. Knowledge Discovery in Databasesâ€¯: An Overview. AI Mag. 13(3): 57-70.
I. Geist. 2002. A Framework for Data Mining and KDD. In Symposium on Applied computing. 508-513.
M. West. 2011. Developing High Quality Data Models. First edit. Elsevier.
K.-S. Wang. 2013. Towards Zero-Defect Manufacturing (ZDM)â€”A Data Mining Approach. Adv. Manuf. 1(1): 62-74.
G. C. Crisan, C. M. Pintea, and C. Chira 2012. Risk Assesment for Incoherent Data. Environ. Eng. Manag. J. 11(12): 2169-2174.
K. Kerdprasop and N. Kerdprasop. 2011. A Data Mining Approach to Automate Fault Detection Model Development in the Semiconductor Manufacturing Process. Int. J. Mech. 5(4): 336-344.
R. Blake and P. Mangiameli. 2011. The Effects and Interactions of Data Quality and Problem Complexity on Classification. J. Data Inf. Qual. 2(2): 1-28.
J. Stang, T. Hartvigsen, and J. Reitan. 2010. The Effect of Data Quality on Data Mining-Improving Prediction Accuracy by Generic Data Cleansing. In International Conference on Information Quality ICIQ.
L. L. Pipino, Y. W. Lee, and R. Y. Wang. 2002. Data Quality Assessment. Commun. ACM. 45(4): 211-218.
H. Alhammady and K. Ramamohanarao. 2004. The Application of Emerging Patterns for Improving the Quality of Rare-Class Classification. In Advances in Knowledge Discovery and Data Mining, Springer Berlin Heidelberg. 207-211.
G. M. Weiss. 2004. Mining with Rarityâ€¯: A Unifying Framework. SIGKDD Explor. Newsl. 6(1): 7-19.
S. Han, B. Yuan, and W. Liu. 2009. Rare Class Miningâ€¯: Progress and Prospect. Chinese Conf. Pattern Recognit. 1-5.
G. M. Weiss. 2010. Mining with Rare Cases. In Data Mining and Knowledge Discovery Handbook. 2nd ed. Springer US, 747-757.
L. Rokach and O. Maimom. 2006. Data Mining for Improving the Quality of Manufacturing: A Feature Set Decomposition Approach. J. Intell. Manuf.17(3): 285-299.
C. C. Teck, L. Xiang, Z. Junhong, and D. Woon. 2012. Hybrid Rebalancing Approach to Handle Imbalanced Dataset for Fault Diagnosis in Manufacturing Systems. In I7th IEEE Conference on Industrial Electronics and Applications (ICIEA). 1224-1229.
X. Guo, Y. Yin, C. Dong, G. Yang, and Guangtong Zhou, 2008. On the Class Imbalance Problem. Fourth Int. Conf. Nat. Comput. 4: 192-201.
L. Rushi, S. D. Snehlata, and M. Latesh. 2013. Class Imbalance Problem in Data Miningâ€¯: Review. Int. J. Comput. Sci. Netw. 2(1).
N. V Chawla, N. Japkowicz, and K. Aleksander. 2004. Editorialâ€¯: Special Issue on Learning from Imbalanced Data Sets. ACM Sigkdd Explor. Newsl. 6(1): 1-6.
S. Wang and X. Yao. 2012. Multiclass Imbalance Problems: Analysis and Potential Solutions. IEEE Trans. Syst. man, Cybern. Part B, Cybern. 42(4): 1119-1130.
A. Symeonidis and P. Mitkas. 2005. Data Mining and Knowledge Discovery: A Brief Overview. In Agent Intelligence Through Data Mining, United States: Springer.
K. Mehmed. 2011. Data Mining: Concepts, Models, Methods, and Algorithms. Second rev. Wiley-Blackwell.
E. W. T. Ngai, Y. Hu, Y. H. Wong, Y. Chen, and X. Sun. 2011. The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of Literature. Decis. Support Syst. 50(3): 559-569.
J. Wu. 2014. Interpretation of Association Rules with Multi-tier Granule Mining. Queensland University of Technology.
MartÃnez-de-PisÃ³n, F. Javier, E. M.-P. AndrÃ©s Sanz, J. Emilio, and C. Dante. 2012. Mining Association Rules from Time Series to Explain Failures in a Hot-Dip Galvanizing Steel Line. Comput. Ind. Eng. 63(1): 22-36.
Yun Sing Koh and R. Nathan. 2009. Rare Association Rule Mining: An Overview. In Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection. Vol 3., Y. S. Koh, Ed. New York: IGI Global. 320.
S. Pisalpanus. 2012. A Landmark Model For Assigning Item Weight For Pattern Mining. Auckland University of Technology.
C. H. Cai, A. W. C. Fu, C. H. Cheng, and W. W. Kwong. 1998. Mining Association Rules with Weighted Items. Int. Database Eng. Appl. Symp. 68-77.
G. D. Ramkumar, S. Ranka, and S. Tsur. 1997 Weighted Association Rules: Model and Algorithm. Proc.ACM SIGKDD. 1-13.
F. Tao, F. Murtagh, and M. Farid. 2003. Weighted Association Rule Mining Using Weighted Support and Significance Framework. Proc. ninth ACM SIGKDD Int. Conf. Knowl. Discov. data Min. 661-666.
W. Wang, J. Yang, and P. Yu. 2004. WAR: Weighted Association Rules for Item Intensities. Knowl. Inf. Syst. 6(2): 203-229.
L. Lin and M. L. Shyu. 2010. Weighted Association Rule Mining for Video Semantic Detection. Int. J. Multimed. Data Eng. Manag. 1(1): 37-54.
W. Jian and L. X. Ming. 2008. An Effective Algorithm for Mining Weighted Association Rules in Telecommunication Networks. J. Comput. 3. 3(10): 20-27.
S. Altuntas and H. Selim. 2012. Facility Layout Using Weighted Association Rule-based Data Mining Algorithms: Evaluation With Simulation. Expert Syst. Appl. 39(1): 3-13.
D. Lee, S. H. Park, and S. Moon. 2013. Utility-based Association Rule Mining: A Marketing Solution for Cross-Selling. Expert Syst. Appl. 40(7): 2715-2725.
K. Sun and F. Bai. 2008. Mining Weighted Association Rules Without Preassigned Weights. IEEE Trans. Knowl. Data Eng. 20(4): 489-495.
Y. S. Koh, R. Pears, and W. Yeap. 2010. Valency Based Weighted Association Rule Mining. Adv. Knowl. Discov. Data Min. 274-285.
R. Pears, Y. S. Koh, and G. Dobbie. 2010. EWGenâ€¯: Automatic Generation of Item Weights for Weighted Association Rule Mining. Adv. Data Min. Appl. 36-47.
M. Padmavalli and K. Sreenivasa Rao. 2013. An Efficient Interesting Weighted Association Rule Mining. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(10): 1059-1064.
J. M. Kleinberg. 1999. Authoritative Sources in a Hyperlinked Environment. Journal of the ACM. 46(5): 604-632.
Y. S. Koh, R. Pears, and G. Dobbie. 2011. Automatic Assignment of Item Weights for Pattern Mining on Data Streams. In Advances in Knowledge Discovery and Data Mining. vol. 6634. Springer Berlin Heidelberg. 387-398.
R. Pears, Y. S. Koh, G. Dobbie, and W. Yeap. 2013. Weighted Association Rule Mining via a Graph Based Connectivity Model. Inf. Sci. (Ny). 218: 61-84.
How to Cite
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.