Document Clustering based on Topic Maps

Abstract

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

Exploiting Document Level Semantics in Document Clustering

“…Increased cluster purity clearly establishes the fact that the features extracted from the three representations capture the semantics of the documents. The three approaches FIHC [6], CFWS [10] and TMHC [12] produced F-measure for the data sets (See Table IV The proposed approach clearly had shown improvement in most of test cases. This is due to the fact that the multiple representations of documents in the collection capture the semantics in a better way, and are able to produce high FMeasure which is an indication of balance precision and recall (See Figure 6).…”

“…It also introduced a novel similarity measure based on common features of the two corresponding graphs of the documents. One more recent approach to capture semantic representation of documents in document representation model is introduced in [12] in which the authors proposed a topic maps based representation by using an online tool Wandora for extracting topics from a document. They also reported encouraging results for document clustering based on semantic notions.…”

“…The approach in [6] proposed a frequent item set-based representation of documents for clustering (FIHC), the second is from [10] from where we only compare with frequent word sequences (CFWS), and third and final is from [12] where authors used topic maps based representation of documents. We have implemented the proposed approaches as described in [6,10,12].…”

A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

“…Ahmadi et al [14] proved that topic model based clustering methods generally achieve better results than only applying traditional clustering algorithms like the K-means. LDA has been used in many papers for representation and dimensionality reduction of text documents, as well as for uncovering semantic relations in the text [15]. Ma et al [16] used LDA for document representation and identification of the most significant topics, the K-means++ algorithm was used to define the initial centers of the clusters and the K-means algorithm was used to form the final clusters.…”

Document clustering with evolved search queries

“…An alternate approach was taken by Rafi et al [18] who introduced a new document representation model based on the compact topic maps that are present in a document.…”