NDLI: Unique distance measure approach for K-means (UDMA-Km) clustering algorithm

Content Provider	IEEE Xplore Digital Library
Author	Pun, W.K.D. Ali, A.S.
Copyright Year	2007
Description	Author affiliation: Central Queensland Univ., Rockhampton (Pun, W.K.D.; Ali, A.S.)
Abstract	Clustering technique in data mining has received a significant amount of attention from machine learning community in the last few years and become one of the fundamental research areas. Among the vast range of clustering algorithms, K-means is one of the most popular clustering algorithms. The basic principle of the K-means algorithm is to know how different distance measure is defined. It is a critical issue for K-means users. For example, how can we select a unique distance measure method for an optimum clustering task? Our research provides a statistical based unique distance measure approach for K- means (UDMA-Km) to this issue. We consider 112 supervised datasets and measure the statistical data characteristics using central tendency measure. Those data characteristics are split using well known entropy method to generate the rules. Finally, the generated rules are used to select the unique distance measure for K-means algorithm. The experiment is conducted within 112 problems and 10 fold cross validation methods. The most significant contribution of our study is that a new algorithm was created and the new algorithm can be used and has been used to solve any clustering tasks very quickly and provide much better optimum performance.
Starting Page	1
Ending Page	4
File Size	201095
Page Count	4
File Format	PDF
ISBN	9781424412716
DOI	10.1109/TENCON.2007.4429131
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-30
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Machine learning algorithms Data mining Data warehouses Algorithm design and analysis Euclidean distance Machine learning Entropy Decision trees Area measurement
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

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