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Clustering for knowledgeable web mining
Anand Rajaraman
Published in Springer Verlag
2015
Volume: 324
   
Pages: 491 - 498
Abstract
Web pages nowadays have different forms and types of content. When the Web content is considered, they are in the form of pictures, videos, audio files, and text files in different languages. The content can be multilingual, heterogeneous, and unstructured. The mining should be independent of the language and software. Statistical features of the images are extracted from the pixel map of the image. The extracted features are presented to the fuzzy clustering algorithm (FCM) and Gath–Geva algorithm. The similarity metric being Euclidean distance and Gaussian distance, respectively. The accuracy is compared and presented. © Springer India 2015.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer Verlag
ISSN21945357
Open AccessNo
Concepts (13)
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    Artificial intelligence
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    Evolutionary algorithms
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    Fuzzy clustering
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    Social networking (online)
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    AUDIO FILES
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    CONTENT MINING
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    Euclidean distance
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    Similarity metrics
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    STATISTICAL FEATURES
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    TEXT FILE
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    WEB CONTENT
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    WEB MINING
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    Clustering algorithms