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UNSUPERVISED DATA MINING BY RECURSIVE PARTITIONING

HE, AIJING
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1026406153

Abstract Details

2002, MS, University of Cincinnati, Engineering : Computer Science.
In this thesis, an experimental investigation into unsupervised database mining was conducted. A novel paradigm for autonomous mining proposed by Dr. L. J. Mazlack was tested. The idea states that increasing coherence will increase conceptual information; and this in turn will reveal previously unrecognized, useful and implicit information. [Mazlack,1996] In the experiments, different partitioning heuristics were tested: arbitrary partition, balanced partition and imbalanced partition. Their usefulness and differences in result are discussed in this thesis. To assist our partitioning heuristics, a rough set based model called Total Roughness was designed to measure the crispness of a partition. This model was used in our experiments to help choose partitioning attribute as well as perform non-scalar data clustering. The feasibility of integrating rough set theory in unsupervised partitioning is evaluated and addressed in this thesis.
Dr. Lawrence J. Mazlack (Advisor)
Computer Science
data mining; recursive partitioning; rough set; unsupervised; knowledge discovery

Recommended Citations

Citations

  • HE, A. (2002). UNSUPERVISED DATA MINING BY RECURSIVE PARTITIONING [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1026406153

    APA Style (7th edition)

  • HE, AIJING. UNSUPERVISED DATA MINING BY RECURSIVE PARTITIONING. 2002. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1026406153.

    MLA Style (8th edition)

  • HE, AIJING. "UNSUPERVISED DATA MINING BY RECURSIVE PARTITIONING." Master's thesis, University of Cincinnati, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1026406153

    Chicago Manual of Style (17th edition)

ucin1026406153
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© 2002, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.