Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature
Jayaraj Jayabharathy and Selvadurai Kanmani
Increase in the number of documents in the corpuses like News groups, government organizations, internet and digital libraries, have led to greater complexity in categorizing and retrieving them. Incorporating semantic features will improve the accuracy of retrieving documents through the method of clustering and which will also pave the way to organize and retrieve the documents more efficiently, from the large available corpuses. Even though clustering based on semantics enhances the quality of clusters, scalability of the system still remains complicated. In this paper, three dynamic document clustering algorithms, namely: Term frequency based MAximum Resemblance Document Clustering (TMARDC), Correlated Concept based MAximum Resemblance Document Clustering (CCMARDC) and Correlated Concept based Fast Incremental Clustering Algorithm (CCFICA) are proposed. From the above three proposed algorithms the TMARDC algorithm is based on term frequency, whereas, the CCMARDC and CCFICA are based on Correlated terms (Terms and their Related terms) concept extraction algorithm. The proposed algorithms were compared with the existing static and dynamic document clustering algorithms by conducting experimental analysis on the dataset chosen from 20Newsgroups and scientific literature. F-measure and Purity have been considered as metrics for evaluating the performance of the algorithms. The experimental results demonstrate that the proposed algorithm exhibit better performance, compared to the four existing algorithms for document clustering.
Year of publication: |
2014
|
---|---|
Authors: | Jayabharathy, Jayaraj ; Kanmani, Selvadurai |
Published in: |
Decision Analytics. - Berlin : SpringerOpen, ISSN 2193-8636, ZDB-ID 2758695-9. - Vol. 1.2014, p. 1-21
|
Subject: | Static and dynamic document clustering | MAximum resemblance data labeling (MARDL) technique | Term frequency | Inverse document frequency (TFIDF) | Concepts | Semantic similarity |
Saved in:
freely available
Extent: | graph. Darst. |
---|---|
Type of publication: | Article |
Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
Language: | English |
Notes: | Systemvoraussetzung: Acrobat Reader |
Other identifiers: | hdl:10419/161810 [Handle] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10010338776
Saved in favorites
Similar items by subject
-
Jayabharathy, Jayaraj, (2014)
-
Automatic Ontology Construction using Conceptualization and Semantic Roles
Arora, Amita, (2017)
-
Multi term based co-term frequency method for term weighting in information retrieval
Santhanakumar, M., (2018)
- More ...
Similar items by person
-
Jayabharathy, Jayaraj, (2014)
- More ...