Title
Boosting Discrimination Information Based Document Clustering Using Consensus and Classification.
Abstract
Adequate choice of term discrimination information measure (DIM) stipulates guaranteed document clustering. Exercise for the right choice is empirical in nature, and characteristics of data in the documents help experts to speculate a viable solution. Thus, a consistent DIM for the clustering is a mere conjecture and demands intelligent selection of the information measure. In this work, we propose an automated consensus building measure based on a text classifier. Two distinct DIMs construct basic partitions of documents and form base clusters. The consensus building measure method uses the clusters information to find concordant documents and constitute a dataset to train the text classifier. The classifier predicts labels for discordant documents from earlier clustering stage and forms new clusters. The experimentation is performed with eight standard data sets to test efficacy of the proposed technique. The improvement observed by applying the proposed consensus clustering demonstrates its superiority over individual results. Relative Risk (RR) and Measurement of Discrimination Information (MDI) are the two discrimination information measures used for obtaining the base clustering solutions in our experiments.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2923462
IEEE ACCESS
Keywords
Field
DocType
Consensus clustering,discrimination information,document clustering,evidence combination,knowledge reuse,mining methods and algorithms,text mining
Computer science,Document clustering,Artificial intelligence,Boosting (machine learning),Machine learning,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Ahmad Muqeem Sheri1214.01
Muhammad Aasim Rafique200.34
Malik Tahir Hassan3194.77
Khurum Nazir Junejo4576.08
Moongu Jeon545672.81