Authors:
A. K. M. Zahiduzzaman
;
Mohammad Nahyan Quasem
;
Faiyaz Ahmed
and
Rashedur M. Rahman
Affiliation:
North South University, Bangladesh
Keyword(s):
Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Factor Analysis (FA), Intensification, c-Means clustering, Fuzzy c-Means clustering.
Related
Ontology
Subjects/Areas/Topics:
Advanced Applications of Fuzzy Logic
;
Applications of Expert Systems
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Web Information Systems and Technologies
Abstract:
The paper presents two document clustering techniques to group Bangla newspaper articles. The first one is based on traditional c-means algorithm, and the later is based on its fuzzy counterpart, i.e., fuzzy c-means algorithm. The key principle for both of those techniques is to measure the frequency of keywords in a particular type of article to calculate the significance of those keywords. The articles are then clustered based on the significance of the keywords. We believe the findings from this research will help to index Bangla newspaper articles. Therefore, the information retrieval will be faster than before. However, one of the challenge is to find the salient features from hundred of features found in documents. Besides, both clustering algorithms work well on lower dimensions. To address this, we use three dimensionality reduction techniques, known as Principle Component Analysis (PCA), Factor Analysis (FA) and Linear Discriminant Analysis (LDA). We present and analyze the
performance of traditional and fuzzy c-means algorithms with different dimensionality reduction techniques.
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