Text categorization is the task of deciding whether a document belongs to a set of prespecified classes of documents. Automatic classification schemes can greatly facilitate the process of categorization. Categorization of documents is challenging, as the number of discriminating words can be very large. Many existing algorithms simply would not work with these many number of features. k-neares...

For traditional way of Web document representation in Vector Space Model, zero-valued similarity problem between vectors occurs frequently, which decreases classificatory quality when defining the relation between Web documents. In this paper, a novel Web document representation and classification approach based on rough set is proposed. Firstly, TF*IDF weighting scheme is used to assign weight...

In this paper, we propose a web document ranking method using topic modeling for effective information collection and classification. The proposed is applied to the technique avoid duplicated crawling when at high speed. Through technique, it feasible remove redundant documents, classify documents efficiently, confirm that crawler service running. enables rapid of many documents; user can searc...

We present an approach to feature weight optimization for document-level decoding. This is an essential task for enabling future development of discourse-level statistical machine translation, as it allows easy integration of discourse features in the decoding process. We extend the framework of sentence-level feature weight optimization to the document-level. We show experimentally that we can...

Information retrieval (IR) is the area of study concerned with searching documents or information within documents. The user describes information needs with a query which consists of a number of words. Finding weight of a query term is useful to determine the importance of a query. Calculating term importance is fundamental aspect of most information retrieval approaches and it is traditionall...

Machine learning and data mining methods are applied to perform large data analysis. Clustering methods are applied to group the related data values. Partitional clustering and hierarchical clustering methods are applied to handle the clustering operations. Tabular format data processing is carried out under the partitional clustering models. Tree based data clustering is adapted in the hierarc...

<p style='text-indent:20px;'>Combinatorial <inline-formula><tex-math id="M1">\begin{document}$ t $\end{document}</tex-math></inline-formula>-designs have been an interesting topic in combinatorics for decades. It is a basic fact that the codewords of fixed weight code may hold id="M2">\begin{document}$ $\end{document}</tex-math></inline-formula>-desi...