Study Limitations

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5 Conclusion

In the end, I reflect on the target purpose setup in the beginning of this study, summarize it and provide possible future work.

5.1 Study Limitations

WEKA is quiet good and open data-mining tool for data analysis, so it is chosen for implementation of this study, but with my limited capability and knowledge, I try my best to increase the adaption be-tween the data and the algorithm, as well as, to invoke most likely procedures in its system built-in environment framework without cus-tomizing it to fit the workflow and algorithm referred.

5.2 Research Contribution

A significant product feature subset to predict process-time in IC substrate is done as the result presented in last section, through the 3 data-mining methods as GR-SNBC (Gain Ratio with Naive Bayes Classifier), SU-SNBC (Symmetrical Uncertainty with Naive Bayes Classifier) and SU-CART (Symmetrical Uncertainty with Classification and Regression Tree Classifier) approach.

Through this result, I could conclude that the process time of IC-substrate in drilling operation quite depends on product characteris-tics rather than processing control. Therefore, in order to decrease the process time, it is reasonable for us to evaluate the factors of product construction than production management.

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5.3 Future Suggestion

In the new era of “Industry 4.0”, prediction and estimation is get-ting more and more important in all manufacturing, the purpose of feature selection, not only precise and accuracy but also the efficiency and easy-to-understand should be contained.

So far, there are basically existing two approaches, a filter ap-proach and a wrapper one. Although a wrapper apap-proach has better satisfaction but takes too much time than filter approach, so how to develop a balance approach which precise, accuracy, efficiency and easy-to-understand should be considered in this subject.

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REFERENCES

[1] Backus, P.; Janakiram, M.; Mowzoon, S.; Runger, G.C.; Bhargava, A. "Factory cycle-time prediction with a data-mining ap-proach", Semiconductor Manufacturing, IEEE Transactions on, On page(s): 252 - 258 Volume: 19, Issue: 2, May 2006 [2] I. Tirkel, "Cycle time prediction in wafer fabrication line by

apply-ing data minapply-ing methods", Proc. 22nd IEEE/SEMIASMC, pp. 1-5, 2011

[3] Y. Meidan , B. Lerner , G. Rabinowitz and M. Hassoun, "Cycle-time key factor identification and prediction in semiconductor man-ufacturing using machine learning and data mining", IEEE Trans.

Semicond. Manuf., vol. 24, no. 2, pp. 237-248, 2011

[4] Chien, C. F., Hsiao, C. W., Meng, C., Hong, K. D., Wang, S. T., 2005. Cycle time prediction and control based on production line status and manufacturing data mining, Proceedings of In-ternational Symposium on Semiconductor Manufacturing Con-ference 2005, 13-15 September, San Jose, California, USA, pp.327-330.

[5] Hassoun, M. "On Improving the Predictability of Cycle Time in an NVM Fab by Correct Segmentation of the Process", Semicon-ductor Manufacturing, IEEE Transactions on, On page(s): 613 - 618 Volume: 26, Issue: 4, Nov. 2013

[6] Dash, M., & Liu, H. (1997). Feature selection for classification. In-telligent Data Analysis, 1(1-4), 131-156.

[7] Liu, H., & Motoda, H. (1998). Feature extraction, construction and

‧ 國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

-46-

selection: A data mining perspective. Norwell,MA: Kluwer Aca-demic Publishers.

[8] Liu, H., & Motoda, H. (1998). Feature selection for knowledge dis-covery and data mining. Norwell, MA: Kluwer Academic Pub-lishers.

[9] A. Whitney, "A direct method of nonparametric measurement se-lection", IEEE Transactions on Computers, vol. 20, pp.1100-1103, 1971

[10] S.-H. Chung and H.-W. Huang, "Cycle time estimation for wafer fab with engineering lots", IIE Trans., vol. 34, pp. 105-118, 2002

[11] Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI maga-zine, 17(3), 37.

[12] Pyle, D. (1999). Data preparation for data mining(Vol. 1). Mor-gan Kaufmann.

[13] Chien, C. F., Hsiao, A., and Wang, I., 2004. Constructing semi-conductor manufacturing performance index and applying data mining for manufacturing data analysis, Journal of Chinese In-stitute of Industrial Engineering, vol.21, pp.313-327.

[14] Chien, C. F., Wang, W. C., and Cheng, J. C., 2007. Data mining for yield enhancement in semiconductor manufacturing and an empirical study, Expert Systems with Applications, vol. 33, pp.

1-7.

[15] Sheng, V. S., Provost, F., & Ipeirotis, P. G. (2008, August). Get another label? improving data quality and data mining using

‧ 國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

-47-

multiple, noisy labelers. InProceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 614-622). ACM.

[16] Lommen, A. (2009). MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Analytical chemistry, 81(8), 3079-3086.

[17] Famili, F., Shen, W. M., Weber, R., & Simoudis, E. (1997). Data pre-processing and intelligent data analysis. International Jour-nal on Intelligent Data AJour-nalysis,1(1).

[18] Chung, S. H., & Huang, H. W. (2002). Cycle time estimation for wafer fab with engineering lots. IIE Transactions, 34(2), 105-118.

[19] Chung, S. H., & Huang, H. W. (1999). The block-based cycle time estimation algorithm for wafer fabrication factories. INTERNA-TIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY AP-PLICATIONS AND PRACTICE, 6(4), 307-316.

[20] Singhal, S., & Jena, M. (2013). A Study on WEKA Tool for Data Preprocessing, Classification and Clustering. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2(6), 250-253.

[21] Her, Jane-Fung & Rong-Kwei Li. (1996). Cycletime Prediction used Stage Regression Method in Wafer Fabrication Fac-tory (Doctoral dissertation).

[22] Cheng, Chao-Ming & Chung, Shu-Hsing (1995). The Design of Due-Date Assignment Model for a Wafer Fabrication Fac-tory (Doctoral dissertation).

‧ 國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

-48-

[23] Chen, T. (2007). An intelligent hybrid system for wafer lot output time prediction. Advanced Engineering Informatics, 21(1), 55-65.

[24] Vig, M. M., & DOOLEY, K. J. (1991). Dynamic rules for due-date assignment. THE INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 29(7), 1361-1377.

[25] Cheng, T. C. E., & Gupta, M. C. (1989). Survey of scheduling research involving due date determination decisions. European Journal of Operational Research, 38(2), 156-166.

[26] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., &

Witten, I. H. (2009). The WEKA data mining software: an up-date. ACM SIGKDD explorations newsletter, 11(1), 10-18.

[27] Tamilselvi, J. J., & Gifta, C. B. (2011). Handling Duplicate Data in Data Warehouse for Data Mining. International Journal of Computer Applications (0975–8887), 15(4).

[28] Piatetsky-Shapiro, G., Brachman, R. J., Khabaza, T., Kloesgen, W., & Simoudis, E. (1996, August). An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery Applications. In KDD (Vol. 96, pp. 89-95).

[29] Chen, Z., Wu, C., Zhang, Y., Huang, Z., Ran, B., Zhong, M., &

Lyu, N. (2015). Feature selection with redundancy-complemen-tariness dispersion.Knowledge-Based Systems, 89, 203-217.

[30] Quinlan, J. R. (1992, November). Learning with continuous clas-ses. In 5th Australian joint conference on artificial intelli-gence (Vol. 92, pp. 343-348).

‧ 國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

-49-

[31] Song, Q., Ni, J., & Wang, G. (2013). A fast clustering-based fea-ture subset selection algorithm for high-dimensional data. IEEE transactions on knowledge and data engineering, 25(1), 1-14.