Clustering Uncertain Data Objects using Jeffreys-Divergence and Maximum Bipartite Matching based Similarity Measure

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018117" target="_blank" >RIV/62690094:18450/21:50018117 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9440910" target="_blank" >https://ieeexplore.ieee.org/document/9440910</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2021.3083969" target="_blank" >10.1109/ACCESS.2021.3083969</a>

Result language
angličtina
Original language name
Clustering Uncertain Data Objects using Jeffreys-Divergence and Maximum Bipartite Matching based Similarity Measure
Original language description
In recent years, uncertain data clustering has become the subject of active research in many fields, for example, pattern recognition, and machine learning. Nowadays, researchers have committed themselves to substitute the traditional distance or similarity measures with new metrics in the existing centralized clustering algorithms in order to tackle uncertainty in data. However, in order to perform uncertain data clustering, representation plays an imperative role. In this paper, a Monte-Carlo integration is adopted and modified to express uncertain data in a probabilistic form. Then three similarity measures are used to determine the closeness between two probability distributions including one novel measure. These similarity measures are derived from the notion of Kullback-Leibler divergence and Jeffreys divergence. Finally, density-based spatial clustering of applications with noise and k-medoids algorithms are modified and implemented on one synthetic database and three real-world uncertain databases. The obtained outcomes confirm that the proposed clustering technique defeats some of the existing algorithms. CCBY
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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