NDLI: Gradual clustering algorithms

Content Provider	IEEE Xplore Digital Library
Author	Fei Wu Gardarin, G.
Copyright Year	2001
Description	Author affiliation: PRiSM Lab., Versailles Univ., Versailles, France (Fei Wu)
Abstract	Clustering is one of the important techniques in data mining. The objective of clustering is to group objects into clusters such that objects within a cluster are more similar to each other than objects in different clusters. The similarity between two objects is defined by a distance function, e.g., the Euclidean distance, which satisfies the triangular inequality. Distance calculation is computationally very expensive and many algorithms have been proposed so far to solve this problem. This paper considers the gradual clustering problem. From practice, we noticed that the user often begins clustering on a small number of attributes, e.g., two. If the result is partially satisfying the user will continue clustering on a higher number of attributes, e.g., ten. We refer to this problem as the gradual clustering problem. In fact gradual clustering can be considered as vertically incremental clustering. Approaches are proposed to solve this problem. The main idea is to reduce the number of distance calculations by using the triangle inequality. Our method first stores in an index the distances between a representative object and objects in n-dimensional space. Then these pre-computed distances are used to avoid distance calculations in (n+m)-dimensional space. Two experiments on real data sets demonstrate the added value of our approaches. The implemented algorithms are based on the DBSCAN algorithm with an associated M-Tree as index tree. However the principles of our idea can well be integrated with other tree structures such as MVP-Tree, R*-Tree, etc., and with other clustering algorithms.
Sponsorship	Informix Software (HK)
Starting Page	48
Ending Page	55
File Size	720157
Page Count	8
File Format	PDF
ISBN	0769509967
DOI	10.1109/DASFAA.2001.916364
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-04-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Partitioning algorithms Data mining Databases Laboratories Euclidean distance Tree data structures Robustness
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