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http://dx.doi.org/10.5392/JKCA.2017.17.11.600

Multi-class Support Vector Machines Model Based Clustering for Hierarchical Document Categorization in Big Data Environment  

Kim, Young Soo (배재대학교 사이버보안학과)
Lee, Byoung Yup (배재대학교 사이버보안학과)
Publication Information
The Journal of the Korea Contents Association / v.17, no.11, 2017 , pp. 600-608 More about this Journal
Abstract
Recently data growth rates are growing exponentially according to the rapid expansion of internet. Since users need some of all the information, they carry a heavy workload for examination and discovery of the necessary contents. Therefore information retrieval must provide hierarchical class information and the priority of examination through the evaluation of similarity on query and documents. In this paper we propose an Multi-class support vector machines model based clustering for hierarchical document categorization that make semantic search possible considering the word co-occurrence measures. A combination of hierarchical document categorization and SVM classifier gives high performance for analytical classification of web documents that increase exponentially according to extension of document hierarchy. More information retrieval systems are expected to use our proposed model in their developments and can perform a accurate and rapid information retrieval service.
Keywords
Big Data; Hierarchical Class; Document Categorization; Clustering; Similarity;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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