NDLI: Fuzzy K- means cluster validation for institutional quality assessment

Content Provider	IEEE Xplore Digital Library
Author	Kumar, S.P. Ramaswami, K.S.
Copyright Year	2010
Description	Author affiliation: Department of Computer Applications, Erode Sengunthar Engineering College, Thudupathi, 57, India (Kumar, S.P.) \|\| Coimbatore Institute of Technology, Civil Aerodrome Post, 641 014, Tamilnadu, India (Ramaswami, K.S.)
Abstract	The most important facts in educational institutional system growth lies in the quality of services rendered. (i.e., faculty profile, student performance and infrastructure requirements). The highest level of quality in educational institution can be achieved by utilizing the managerial decision makers with valuable implicit knowledge, which is currently unknown /hidden to them. The knowledge hidden among the educational data set is extractable through data mining technology. Clustering, an unsupervised learning depends on certain initiation values to define the subgroups present in a data set. Based on the features of the dataset and input parameters cluster formation may vary, which motivates the clarification of cluster validity. The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. The cluster validation criterion is introduced to find the optimal input metrics for fuzzy k-means algorithm. Validation criteria focus on the quality metrics of the institution features for cluster formation and handle efficiently the arbitrary shaped clusters. Experimental results show improved stability and accuracy for clustering structures obtained via sub sampling, and adaptive techniques. These improvements offer insights into specific decision within the data sets. The experimental results confirm the reliability of validity index showing that it performs favorably in all cases selecting independently of clustering algorithm the scheme that best fits the data under consideration.
Starting Page	628
Ending Page	635
File Size	427853
Page Count	8
File Format	PDF
ISBN	9788183713696
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-27
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Kongu Engineering College
Subject Keyword	Databases Clustering algorithms Educational institutions Data Mining Cluster Quality assessment Data mining Decision tree Classification tree analysis Educational institution
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
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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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