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Decisions fusion strategy towards hybrid cluster ensemble /

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Decisions fusion strategy towards hybrid cluster ensemble /
นักวิจัย : Hassan, Zahid. , Verma, Brijesh.
คำค้น : Data mining. , Not a CQU Research Flagship , 700101 Application packages , 700102 Application tools and system utilities , 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic , 280207 Pattern Recognition , 280208 Computer Vision , Neural networks (Computer science) , Computer vision. , Application software. , Clustering ensembles -- Neural fusion algorithms -- Data mining
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2550
อ้างอิง : http://hdl.cqu.edu.au/10018/12508 , http://acquire.cqu.edu.au:8080/vital/access/manager/Repository/cqu:2717 , cqu:2717
ที่มา : Hassan, Z & Verma, B 2007, 'Decisions Fusion Strategy: Towards Hybrid Cluster Ensemble', 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing Melbourne, Australia, 3–6 December 2007, IEEE, pp. 377-382.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia, 3-6 December 2007 USA : IEEE, 2007. p.377-382 6 pages 1424415020 , aCQUIRe [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

Clustering ensembles have renowned as a powerful methodfor improving both the performance and constancy ofunsupervised classification solutions. However, finding aconsensus clustering from multiple algorithms is a difficultproblem that can be approached from combinatorial orstatistical perspectives. We offer a new clustering strategywhich is formulated to cluster extracted mammographyfeatures into soft clusters using unsupervised learningstrategies and ‘fuse’ the decisions using majority voting andparallel fusion in conjunction with a neural classifier. Theidea is to observe associations in the features and fuse thedecisions (made by learning algorithms) to find the strongclusters which can make impact on overall classificationaccuracy. Two novel techniques are proposed for fusion,majority-voting based data fusion, and neural-based fusion.The proposed approaches are tested and evaluated on thebenchmark database— digital database for screeningmammograms (DDSM). This study compares the performanceof the proposed ensemble approach with other fusionapproaches for clustering ensembles. Experimental resultsdemonstrate the effectiveness of the proposed method onbenchmark dataset.

บรรณานุกรม :
Hassan, Zahid. , Verma, Brijesh. . (2550). Decisions fusion strategy towards hybrid cluster ensemble /.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Hassan, Zahid. , Verma, Brijesh. . 2550. "Decisions fusion strategy towards hybrid cluster ensemble /".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Hassan, Zahid. , Verma, Brijesh. . "Decisions fusion strategy towards hybrid cluster ensemble /."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2550. Print.
Hassan, Zahid. , Verma, Brijesh. . Decisions fusion strategy towards hybrid cluster ensemble /. กรุงเทพมหานคร : Central Queensland University, Australia; 2550.