NDLI: Automatic cluster labeling through Artificial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Lopes, L.A. Machado, V.P. Rabelo, R.D.A.L.
Copyright Year	2014
Description	Author affiliation: Comput. Dept., Fed. Univ. of Piaui, Teresina, Brazil (Lopes, L.A.; Machado, V.P.; Rabelo, R.D.A.L.)
Abstract	The clustering problem has been considered as one of the most important problems among those existing in the research area of unsupervised learning (a Machine Learning subarea). Although the development and improvement of algorithms that deal with this problem has been focused by many researchers, the main goal remains undefined: the understanding of generated clusters. As important as identifying clusters is to understand its meaning. A good cluster definition means a relevant understanding and can help the specialist to study or interpret data. Facing the problem of comprehend clusters - in other words, create labels - this paper presents a methodology to automatic labeling clusters based on techniques involving supervised and unsupervised learning plus a discretization model. Considering the problem from its inception, the problem of understanding clusters is dealt similar to a real problem, being initialized from clustering data. For this, an unsupervised learning technique is applied and then a supervised learning algorithm will detect which are the relevant attributes in order to define a specific cluster. Additionally, some strategies are used to create a methodology that presents a label (based on attributes and their values) for each cluster provided. Finally, this methodology is applied in four distinct databases presenting good results with an average above 88.79% of elements correctly labeled.
Starting Page	762
Ending Page	769
File Size	290375
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889949
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Databases Labeling Unsupervised learning Artificial neural networks Glass Training
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
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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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