NDLI: Robust Local Triangular Kernel density-based clustering for high-dimensional data

Content Provider	IEEE Xplore Digital Library
Author	Musdholifah, A. Hashim, S.Z.M. Ngah, R.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Electron., Univ. Gadjah Mada (UGM), Yogyakarta, Indonesia (Musdholifah, A.) \|\| Wireless Commun. Centre, Univ. Teknol. Malaysia (UTM), Skudai, Malaysia (Ngah, R.) \|\| Soft Comput. Res. Group, Univ. Teknol. Malaysia (UTM), Skudai, Malaysia (Hashim, S.Z.M.)
Abstract	A number of clustering algorithms can be employed to find clusters in multivariate data. However, the effectiveness and efficiency of the existing algorithms are limited, since the respective data has high dimension, contain large amount of noise and consist of clusters with arbitrary shapes and densities. In this paper, a new kernel density-based clustering algorithm, called Local Triangular Kernel-based Clustering (LTKC), is proposed to deal with these conditions. LTKC is based on combination of k-nearest-neighbor density estimation and triangular kernel density-based clustering. The advantages of our LTKC approach are: (1) it has a firm mathematical basis; (2) it requires only one parameter, number of neighbors; (3) it defines the number of cluster automatically; (4) it allows discovering clusters with arbitrary shapes and densities ;and (5) it is significantly faster than existing algorithms. LTKC is tested using artificial data and applied to some UCI data. A comparison with k-means, KFCM and well known density-based clustering algorithms including ILGC, DBSCAN, and DENCLUE shows the superiority of our proposed LTKC algorithm.
Starting Page	24
Ending Page	32
File Size	545020
Page Count	9
File Format	PDF
ISBN	9781467358255
DOI	10.1109/CSIT.2013.6588753
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-03-27
Publisher Place	Jordan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	kernel density estimation Shape density-based clustering Estimation high-dimensional data Classification algorithms k-nearest-neighbor density estimation Clustering Accuracy Clustering algorithms Classification multivariate data Density functional theory Kernel
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