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TREATMENT OF DATA WITH MISSING ELEMENTS IN PROCESS MODELLING

RAPUR, NIHARIKA
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1060192778

Abstract Details

2003, MS, University of Cincinnati, Engineering : Industrial Engineering.
Advancements in monitoring and control have led to the collection of vast amounts of information for analysis. Inherent failure or human error in machines and processes, leads to a large amount of missing information is the data collected for analysis. This research pertains to improving the quality of a dataset by generating missing values, based on the observed data. Many methods have been proposed for the problem of incomplete information. These methods include single imputation techniques, multiple imputation techniques, principal components analysis based methods, and neural networks based methods. But these methods have inherent limitations that restrict their usage. Two areas, the initialization method and the iteration process, were identified as the key areas where efficiency could be improved. A new technique which uses clustering as a tool for generation of missing values was constructed. Clustering techniques like Fuzzy C Means and subtractive clustering based algorithms for missing information were developed. In order to be applied to real life data, these algorithms must be able to predict the missing values fast and with accuracy. This study involves comparing existing methods and new techniques in terms of accuracy of prediction and computational time. Through comparative studies it was shown that subtractive clustering method performs better and faster clustering of data. It reaches higher levels of convergence and is thus a better option to using FCM clustering for the generation of missing values. As an initialization method, clustering based techniques predict initial values that are closer to the actual values in the datasets used for testing. Tests conducted on industrial datasets also show considerable improvement in accuracy and speed of predicting missing values. Tests on incomplete data collected for silicon micromachined atomizer development, show that the accuracy of prediction for clustering based algorithms are of the same distribution as the original observed data. In this study, clustering based algorithms have superior performance statistics in comparison to the existing methods.
Dr. Samuel H. Huang (Advisor)
102 p.
Engineering, Industrial
missing data; data; complete; incomplete; data cleansing

Recommended Citations

Citations

  • RAPUR, N. (2003). TREATMENT OF DATA WITH MISSING ELEMENTS IN PROCESS MODELLING [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1060192778

    APA Style (7th edition)

  • RAPUR, NIHARIKA. TREATMENT OF DATA WITH MISSING ELEMENTS IN PROCESS MODELLING. 2003. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1060192778.

    MLA Style (8th edition)

  • RAPUR, NIHARIKA. "TREATMENT OF DATA WITH MISSING ELEMENTS IN PROCESS MODELLING." Master's thesis, University of Cincinnati, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1060192778

    Chicago Manual of Style (17th edition)

ucin1060192778
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© 2003, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.