Adaptive text-data clustering by the semi-supervised learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F09%3A00147093" target="_blank" >RIV/62156489:43110/09:00147093 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Adaptive text-data clustering by the semi-supervised learning
Original language description
Many current real-world applications as, for example, Internet e-commerce, have to deal with huge volumes of predominantly textual data. The data include hidden information like potential categories of similar customers, suppliers, producers, etc. Thesecategories usually can change during the time. The paper discusses the possibilities of looking for clusters that represent individual classes. One of the main problems is the standard clustering methods give often unsatisfying results by unsupervised-learning methods. However, having very small initial subsets of good examples, the clustering can dramatically improve its results, providing much better clusters applicable to categorizing in the future. This method is known as the semi-supervised learning (SSL) from a limited number of examples. In the paper, some results of applying the SSL method to real-world unlabeled data instances are demonstrated and compared with selected traditional clustering algorithms. Using labeled examples
Czech name
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Czech description
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Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
ASIS 2009 -- Adaptívne siete v informačných systémoch
ISBN
978-80-8094-593-0
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
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Publisher name
Nitra
Place of publication
Univerzita Konštantína Filozofa v Nitre
Event location
Nitra
Event date
Jan 1, 2009
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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