NDLI: Improvement in k-Means Clustering Algorithm Using Data Clustering

Content Provider	IEEE Xplore Digital Library
Author	Rajeswari, K. Acharya, O. Sharma, M. Kopnar, M. Karandikar, K.
Copyright Year	2015
Description	Author affiliation: Pimpri Chinchwad Coll. of Eng., Pune, India (Rajeswari, K.) \|\| Chinchwad Coll. of Eng., Pune, India (Acharya, O.; Sharma, M.; Kopnar, M.; Karandikar, K.)
Abstract	The set of objects having same characteristics are organized in groups and clusters of these objects reformed known as Data Clustering. It is an unsupervised learning technique for classification of data. K-means algorithm is widely used and famous algorithm for analysis of clusters. In this algorithm, n number of data points are divided into k clusters based on some similarity measurement criterion. K-Means Algorithm has fast speed and thus is used commonly clustering algorithm. Vector quantization, cluster analysis, feature learning are some of the application of K-Means. However results generated using this algorithm are mainly dependant on choosing initial cluster centroids. The main short come of this algorithm is to provide appropriate number of clusters. Provision of number of clusters before applying the algorithm is highly impractical and requires deep knowledge of clustering field. In this project, we are going to propose an algorithm for improvement in the initializing the centroids for K-Means algorithm. We are going to work on numerical data sets along with the categorical datasets with the n dimensions. For similarity measurement we are going to consider the Manhattan distance,Dice distance and cosine distance. The result of this proposed algorithm will be compared with the original K-Means. Also the quality and complexity of the proposed algorithm will be checked with the existing algorithm.
Starting Page	367
Ending Page	369
File Size	220377
Page Count	3
File Format	PDF
ISBN	9781479968923
DOI	10.1109/ICCUBEA.2015.205
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-02-26
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computers Centroid Data Clustering Communities K-Means Clustering algorithms Linear programming Electronic mail Complexity theory Unsupervised learning
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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