NDLI: GACH: a grid-based algorithm for hierarchical clustering of high-dimensional data

Content Provider	Springer Nature Link
Author	Mansoori, Eghbal G.
Copyright Year	2013
Abstract	This paper proposes a grid-based hierarchical clustering algorithm (GACH) as an efficient and robust method to explore clusters in high-dimensional data with no prior knowledge. It discovers the initial positions of the potential clusters automatically and then combines them hierarchically to obtain the final clusters. In this regard, GACH first projects the data patterns on a two-dimensional space (i.e., on a plane established by two features) to overcome the curse of dimensionality problem in high-dimensional data. To choose these two well-informed features, a simple and fast feature selection algorithm is proposed. Then, through meshing the plane with grid lines, GACH detects the crowded grid points. The nearest data patterns around these grid points are considered as initial members of some potential clusters. By returning the patterns back to their true dimensions, GACH refines these clusters. In the merging phase, GACH combines the closely adjacent clusters in a hierarchical bottom-up manner to construct the final clusters’ members. The main features of GACH are: (1) it automatically discovers the clusters, (2) the obtained clusters are stable, (3) it is efficient for data sets with high dimensions, and (4) its merging process involves a threshold which can be obtained in advance for well-clustered data. To assess our proposed algorithm, it is applied on some benchmark data sets and the validity of obtained clusters is compared with the results of some other clustering algorithms. This comparison shows that GACH is accurate, efficient and feasible to discover clusters in high-dimensional data.
Starting Page	905
Ending Page	922
Page Count	18
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	18
Issue Number	5
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-08-11
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Grid-based clustering Hierarchical clustering Feature selection High-dimensional data Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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
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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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