3 research outputs found
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) to represent the document in such a form that inherently
captures semantics of the text. This may also help to reduce dimensionality of
the document, and (2) to define a similarity measure based on the semantic
representation such that it assigns higher numerical values to document pairs
which have higher semantic relationship. Feature space of the documents can be
very challenging for document clustering. A document may contain multiple
topics, it may contain a large set of class-independent general-words, and a
handful class-specific core-words. With these features in mind, traditional
agglomerative clustering algorithms, which are based on either Document Vector
model (DVM) or Suffix Tree model (STC), are less efficient in producing results
with high cluster quality. This paper introduces a new approach for document
clustering based on the Topic Map representation of the documents. The document
is being transformed into a compact form. A similarity measure is proposed
based upon the inferred information through topic maps data and structures. The
suggested method is implemented using agglomerative hierarchal clustering and
tested on standard Information retrieval (IR) datasets. The comparative
experiment reveals that the proposed approach is effective in improving the
cluster quality