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Mutual k Nearest Neighbor based Classifier

Gupta, Nidhi

Abstract Details

2010, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
In this information intensive world, data is used to make statistical decisions for business, scientific, and industrial situations. With decreasing cost of computing power and storage, data with various properties are available to data miners for classification type of decisions. So far the classifiers designed to classify the datasets with varying data density required too many parameters and the methods did not work very well. In this research we have introduced a new idea called the Mutual k-Nearest Neighbor (Mk-NN) relationship and designed classifiers based on this idea. We have validated our results with a number of synthetic and a real-world data set and have received superior results. We present these result of the classifications in this thesis and also describe our approach and algorithms.
Raj Bhatnagar, PhD (Committee Chair)
Ali Minai, PhD (Committee Member)
John Schlipf, PhD (Committee Member)
71 p.

Recommended Citations

Citations

  • Gupta, N. (2010). Mutual k Nearest Neighbor based Classifier [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369

    APA Style (7th edition)

  • Gupta, Nidhi. Mutual k Nearest Neighbor based Classifier. 2010. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369.

    MLA Style (8th edition)

  • Gupta, Nidhi. "Mutual k Nearest Neighbor based Classifier." Master's thesis, University of Cincinnati, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369

    Chicago Manual of Style (17th edition)