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Viktoriia O. Samitova

Work place: Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

E-mail: samitova@gmail.com

Website:

Research Interests: Data Structures and Algorithms

Biography

Viktoriia Samitova graduated from Kharkiv National University of Radio Electronics in 2007. She is a PhD student in Computer Science at Kharkiv National University of Radio Electronics. Her current interests are Fuzzy Clustering for Categorical Data.

Author Articles
Possibilistic Fuzzy Clustering for Categorical Data Arrays Based on Frequency Prototypes and Dissimilarity Measures

By Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Viktoriia O. Samitova

DOI: https://doi.org/10.5815/ijisa.2017.05.07, Pub. Date: 8 May 2017

Fuzzy clustering procedures for categorical data are proposed in the paper. Most of well-known conventional clustering methods face certain difficulties while processing this sort of data because a notion of similarity is missing in these data. A detailed description of a possibilistic fuzzy clustering method based on frequency-based cluster prototypes and dissimilarity measures for categorical data is given.

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Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing

By Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Viktoriia O. Samitova

DOI: https://doi.org/10.5815/ijisa.2017.02.01, Pub. Date: 8 Feb. 2017

A task of clustering data given on the ordinal scale under conditions of overlapping clusters has been considered. It’s proposed to use an approach based on membership and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions.

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Fuzzy Clustering Data Given in the Ordinal Scale

By Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Viktoriia O. Samitova

DOI: https://doi.org/10.5815/ijisa.2017.01.07, Pub. Date: 8 Jan. 2017

A fuzzy clustering algorithm for multidimensional data is proposed in this article. The data is described by vectors whose components are linguistic variables defined in an ordinal scale. The obtained results confirm the efficiency of the proposed approach.

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