Web Document Clustering Based on the Clusters of Topic Words

Document Clustering based on Topic Maps

Importance of document clustering is now widely acknowledged by researchers for better management, smart navigation, efficient filtering, and concise summarization of large collection of documents like World Wide Web (WWW). The next challenge lies in semantically performing clustering based on the semantic contents of the document. The problem of document clustering has two main components: (1)...

Web Document Clustering based on Document Structure

Document clustering techniques mostly rely on single term analysis of the document data set, such as the Vector Space Model. To achieve more accurate document clustering, document structure should be reflected in the underlying data model. This paper presents a framework for web document clustering based on two important concepts. The first one is the web document structure, which is currently ...

Document Clustering Method Based on Frequent Co-occurring Words

This paper presents a new document clustering method based on frequent co-occurring words. We first employ the Singular Value Decomposition, and then group the words into clusters called word representatives as substitution of the corresponding words in the original documents. Next, we extract the frequent word representative sets by Apriori. Subsequently, each document is designated to a basic...

Document Clustering Using Topic Analysis

Towards Clustering of Web-based Document Structures

Methods for organizing web data into groups in order to analyze web-based hypertext data and facilitate data availability are very important in terms of the number of documents available online. Thereby, the task of clustering web-based document structures has many applications, e.g., improving information retrieval on the web, better understanding of user navigation behavior, improving web use...