NDLI: On High Dimensional Projected Clustering of Uncertain Data Streams

Content Provider	IEEE Xplore Digital Library
Author	Aggarwal, C.C.
Copyright Year	2009
Abstract	In this paper, we will study the problem of projected clustering of uncertain data streams. The use of uncertainty is especially important in the high dimensional scenario, because the sparsity property of high dimensional data is aggravated by the uncertainty. The uncertainty information is important for not only the determination of the assignment of data points to clusters, but also that of the valid projections across which the data is naturally clustered. The problem is especially challenging in the case where the data is not available on disk and arrives in the form of a fast stream. In such cases, the one-pass constraint in data stream computation poses special challenges to the algorithmic sophistication required for incorporating uncertainty information into the high dimensional computations. We will show that the projected clustering problem can be effectively solved in the context of uncertain data streams.
Starting Page	1152
Ending Page	1154
File Size	204406
Page Count	3
File Format	PDF
ISBN	9781424434220
ISSN	10844627
DOI	10.1109/ICDE.2009.188
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Clustering algorithms Data mining Fading Statistics Data engineering USA Councils Probability density function Quality management Design methodology high dimensionality projected clustering uncertain data
Content Type	Text
Resource Type	Article
Subject	Signal Processing Information Systems Software

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 Framework for Clustering Massive-Domain Data Streams

A Density Grid-Based Clustering Algorithm for Uncertain Data Streams

Tracking High Quality Clusters over Uncertain Data Streams

Density-Based Probabilistic Clustering of Uncertain Data

Uncertain data cluster based on DBSCAN

Customized DBSCAN for Clustering Uncertain Objects

A study of density-grid based clustering algorithms on data streams

Improved Bisector pruning for uncertain data mining

A Hierarchical Algorithm for Clustering Uncertain Data via an Information-Theoretic Approach

On High Dimensional Projected Clustering of Uncertain Data Streams

Similar Documents

A Framework for Clustering Massive-Domain Data Streams

A Density Grid-Based Clustering Algorithm for Uncertain Data Streams

Tracking High Quality Clusters over Uncertain Data Streams

Density-Based Probabilistic Clustering of Uncertain Data

Uncertain data cluster based on DBSCAN

Customized DBSCAN for Clustering Uncertain Objects

A study of density-grid based clustering algorithms on data streams

Improved Bisector pruning for uncertain data mining

A Hierarchical Algorithm for Clustering Uncertain Data via an Information-Theoretic Approach

On High Dimensional Projected Clustering of Uncertain Data Streams