NDLI: Data Clustering with Partial Supervision

Content Provider	Springer Nature Link
Author	BOUCHACHIA, ABDELHAMID PEDRYCZ, WITOLD
Copyright Year	2005
Abstract	Clustering with partial supervision finds its application in situations where data is neither entirely nor accurately labeled. This paper discusses a semi-supervised clustering algorithm based on a modified version of the fuzzy C-Means (FCM) algorithm. The objective function of the proposed algorithm consists of two components. The first concerns traditional unsupervised clustering while the second tracks the relationship between classes (available labels) and the clusters generated by the first component. The balance between the two components is tuned by a scaling factor. Comprehensive experimental studies are presented. First, the discrimination of the proposed algorithm is discussed before its reformulation as a classifier is addressed. The induced classifier is evaluated on completely labeled data and validated by comparison against some fully supervised classifiers, namely support vector machines and neural networks. This classifier is then evaluated and compared against three semi-supervised algorithms in the context of learning from partly labeled data. In addition, the behavior of the algorithm is discussed and the relation between classes and clusters is investigated using a linear regression model. Finally, the complexity of the algorithm is briefly discussed.
Starting Page	47
Ending Page	78
Page Count	32
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	12
Issue Number	1
e-ISSN	1573756X
Language	English
Publisher	Springer US
Publisher Date	2006-02-03
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	clustering partial supervision classification class discrimination linear regression. Information Storage and Retrieval Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Statistics Artificial Intelligence (incl. Robotics) Computing Methodologies Data Mining and Knowledge Discovery
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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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
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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