NDLI: A Boundary Detection algorithm of clusters based on Dual Threshold Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Baozhi Qiu Shuang Wang
Copyright Year	2011
Abstract	Boundary points detection of clusters is important in image processing, machine learning and so on. We propose a boundary detecting algorithm called BDDTS (Boundary Detection algorithm of clusters based on Dual Threshold Segmentation), which is based on the characteristics of the distribution of boundary points. The algorithm, which firstly accrues to the different cost function values of data points, then divides data set into the internal point set, the intermediate point set and the external point set. Secondly by removing the internal points from intermediate point set and combining it to external point set, we get candidate boundary set. At last, we exploit the secondary processing for the candidate boundary set in order to obtain a more accurate boundary. The experimental results show that BDDTS can detect boundary points of clusters in arbitrary shapes, size and densities very rapidly and efficiently. It is also applicable to real data set and high-dimensional data set.
Starting Page	1246
Ending Page	1250
File Size	380488
Page Count	5
File Format	PDF
ISBN	9781457720086
DOI	10.1109/CIS.2011.276
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Boolean functions Shape Noise Boundary Points Clustering algorithms candidate boundary set Data structures Double Threshold Complexity theory Clustering Criterion Function
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

Scalable parallel OPTICS data clustering using graph algorithmic techniques

Data Stream Clustering Algorithm Based on Active Grid Density

Multi-Density Clustering Algorithm Based on Grid and Boundary

An efficient fractals-based algorithm for clustering

A new online clustering approach for data in arbitrary shaped clusters

A clustering algorithm based on building a density-tree

Part Priority Clustering Algorithm for Large-Scale Data Set

A density based algorithm for discovering clusters with varied density

An Adaptive Clustering Algorithm For Image Segmentation

A Boundary Detection algorithm of clusters based on Dual Threshold Segmentation

Similar Documents

Scalable parallel OPTICS data clustering using graph algorithmic techniques

Data Stream Clustering Algorithm Based on Active Grid Density

Multi-Density Clustering Algorithm Based on Grid and Boundary

An efficient fractals-based algorithm for clustering

A new online clustering approach for data in arbitrary shaped clusters

A clustering algorithm based on building a density-tree

Part Priority Clustering Algorithm for Large-Scale Data Set

A density based algorithm for discovering clusters with varied density

An Adaptive Clustering Algorithm For Image Segmentation

A Boundary Detection algorithm of clusters based on Dual Threshold Segmentation