Kusiak, A., "Decomposition in Data Mining: An Industrial Case Study", IEEE Transactions on Electronics Packaging Manufacturing, Vol. 23, No. 4, pp. 345-353, 2000. Home/Search Document Details and Download
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Improving Supervised Learning by Feature Decomposition - Maimon, Rokach (Correct)
....to Michie [30] finding a good decomposition is a major tactic both for ensuring the transparent end product and for avoiding the combinatorial explosion. Decomposition methodology can be considered as effective strategy for changing the representation of a learning problem. In fact Kusiak [22] consider decomposition as the most useful form of transformation of data set . It is generally believed that problem decomposition s benefits from: conceptual simplification of the problem, making the problem more feasible by reducing its dimensionality, achieving clearer results (more ....
Kusiak, A., Decomposition in Data Mining: An Industrial Case Study, IEEE Transactions on Electronics Packaging Manufacturing, Vol. 23, No. 4, 2000, pp. 345-353
Data Mining of Printed-Circuit Board Defects - Kusiak, al. (2001) Self-citation (Kusiak) (Correct)
....Approximately 4.5 of all circuit boards manufactured are found to contain solder ball defects. For details of electronic assembly process, the reader may refer to [1] 4] The research reported in the paper is based on the developments in machine learning and data mining (e.g. 5] 6] and [7]) Some of the best known learning algorithms are as follows. ID3: Induction decision tree is a supervised learning algorithm [8] AQ15: Inductive learning system generates decision rules, where the conditional part is a logical formula [9] Domain knowledge is used to generate new ....
A. Kusiak, "Decomposition in data mining: An industrial case study," IEEE Trans. Electron. Packag. Manufact., vol. 23, pp. 345--353, Oct. 2000.
Rough Set Theory: A Data Mining Tool for Semiconductor.. - Kusiak Self-citation (Kusiak) (Correct)
....b) bootstrapping; c) cross validation. The partitioning method is based on splitting the data into a test set and a training set. The two separate data sets could be created at the data collection phase (a priori partitioning) or after the data has been collected (a posteriori partitioning) [19]. The classification quality derived from a single test set could be questioned, therefore the posteriori partitioning is repeated numerous times. Rather than arbitrarily determining the size of the test data set, the bootstrapping method suggests splitting the data set according to the following ....
A. Kusiak, "Decomposition in data mining: An industrial case study," IEEE Trans. Electron. Packag. Manufact., vol. 23, pp. 345--353, Oct. 2000.
Pattern Analysis and Applications manuscript No. - Will Be Inserted (Correct)
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Kusiak, A., "Decomposition in Data Mining: An Industrial Case Study", IEEE Transactions on Electronics Packaging Manufacturing, Vol. 23, No. 4, pp. 345-353, 2000.
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