NDLI: A min-max cut algorithm for graph partitioning and data clustering

Content Provider	IEEE Xplore Digital Library
Author	Ding, C.H.Q. Xiaofeng He Hongyuan Zha Ming Gu Simon, H.D.
Copyright Year	2001
Description	Author affiliation: NERSC Div., Lawrence Berkeley Lab., CA, USA (Ding, C.H.Q.)
Abstract	An important application of graph partitioning is data clustering using a graph model - the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary information for clustering. In this paper, we propose a new algorithm for graph partitioning with an objective function that follows the min-max clustering principle. The relaxed version of the optimization of the min-max cut objective function leads to the Fiedler vector in spectral graph partitioning. Theoretical analyses of min-max cut indicate that it leads to balanced partitions, and lower bounds are derived. The min-max cut algorithm is tested on newsgroup data sets and is found to out-perform other current popular partitioning/clustering methods. The linkage-based refinements to the algorithm further improve the quality of clustering substantially. We also demonstrate that a linearized search order based on linkage differential is better than that based on the Fiedler vector, providing another effective partitioning method.
Sponsorship	IEEE Comput. Soc. Tech. Committe on Pattern Anal. & Machine Intelligence (TCPAMI)
Starting Page	107
Ending Page	114
File Size	815014
Page Count	8
File Format	PDF
ISBN	0769511198
DOI	10.1109/ICDM.2001.989507
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-11-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Partitioning algorithms Helium Laboratories Computer science Mathematics Application software Mathematical model Testing Clustering methods
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

A min-max cult algorithm for graph partitioning and data clustering.

A min-max cut algorithm for graph partitioning and data clustering (2001).

A study on continuous max-flow and min-cut approaches

Clustering for DNA Microarray Data Analysis with a Graph Cut Based Algorithm

Spectral min-max cut for graph partitioning and data clustering

Spectral min-max cut for graph partitioning and data clustering

K-means clustering using Max-min distance measure

A Local Clustering Algorithm for Massive Graphs and Its Application to Nearly Linear Time Graph Partitioning

A Document Clustering Method Based on Hierarchical Algorithm with Model Clustering

A min-max cut algorithm for graph partitioning and data clustering

Similar Documents

A min-max cult algorithm for graph partitioning and data clustering.

A min-max cut algorithm for graph partitioning and data clustering (2001).

A study on continuous max-flow and min-cut approaches

Clustering for DNA Microarray Data Analysis with a Graph Cut Based Algorithm

Spectral min-max cut for graph partitioning and data clustering

Spectral min-max cut for graph partitioning and data clustering

K-means clustering using Max-min distance measure

A Local Clustering Algorithm for Massive Graphs and Its Application to Nearly Linear Time Graph Partitioning

A Document Clustering Method Based on Hierarchical Algorithm with Model Clustering

A min-max cut algorithm for graph partitioning and data clustering