NDLI: Decisions Fusion Strategy: Towards Hybrid Cluster Ensemble

Content Provider	IEEE Xplore Digital Library
Author	Hassan, S.Z. Verma, B.
Copyright Year	2007
Description	Author affiliation: Central Queensland Univ., Rockhampton (Hassan, S.Z.; Verma, B.)
Abstract	Clustering ensembles have renowned as a powerful method for improving both the performance and constancy of unsupervised classification solutions. However, finding a consensus clustering from multiple algorithms is a difficult problem that can be approached from combinatorial or statistical perspectives. We offer a new clustering strategy which is formulated to cluster extracted mammography features into soft clusters using unsupervised learning strategies and 'fuse' the decisions using majority voting and parallel fusion in conjunction with a neural classifier. The idea is to observe associations in the features and fuse the decisions (made by learning algorithms) to find the strong clusters which can make impact on overall classification accuracy. Two novel techniques are proposed for fusion, majority-voting based data fusion, and neural-based fusion. The proposed approaches are tested and evaluated on the benchmark database - digital database for screening mammograms (DDSM). This study compares the performance of the proposed ensemble approach with other fusion approaches for clustering ensembles. Experimental results demonstrate the effectiveness of the proposed method on benchmark dataset.
Starting Page	377
Ending Page	382
File Size	527345
Page Count	6
File Format	PDF
ISBN	9781424415014
DOI	10.1109/ISSNIP.2007.4496873
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuses Decision making Clustering algorithms Feature extraction Mammography Data mining Unsupervised learning Medical diagnostic imaging Cancer Biomedical imaging
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Autonomous decision-making: a data mining approach

Parallel neural-based hybrid data mining ensemble

Clustering Ensemble for Unsupervised Feature Selection

Ensemble Method for Unsupervised Feature Selection

Self-organizing maps for masking mammography images

Application of fuzzy cluster analysis for medical image data mining

Unsupervised learning neural networks with applications to data fusion

Ensemble decision making system for breast cancer data.

Cascading GA & CFS for Feature Subset selection in Medical Data Mining

Decisions Fusion Strategy: Towards Hybrid Cluster Ensemble

Similar Documents

Autonomous decision-making: a data mining approach

Parallel neural-based hybrid data mining ensemble

Clustering Ensemble for Unsupervised Feature Selection

Ensemble Method for Unsupervised Feature Selection

Self-organizing maps for masking mammography images

Application of fuzzy cluster analysis for medical image data mining

Unsupervised learning neural networks with applications to data fusion

Ensemble decision making system for breast cancer data.

Cascading GA & CFS for Feature Subset selection in Medical Data Mining

Decisions Fusion Strategy: Towards Hybrid Cluster Ensemble