NDLI: Finding the Optimal Subspace for Clustering

Content Provider	IEEE Xplore Digital Library
Author	Goebl, Sebastian He, Xiao Plant, Claudia Bohm, Christian
Copyright Year	2014
Abstract	The ability to simplify and categorize things is one of the most important elements of human thought, understanding, and learning. The corresponding explorative data analysis techniques -- dimensionality reduction and clustering -- have initially been studied by our community as two separate research topics. Later algorithms like CLIQUE, ORCLUS, 4C, etc. Performed clustering and dimensionality reduction in a joint, alternating process to find clusters residing in low-dimensional subspaces. Such a low-dimensional representation is extremely useful, because it allows us to visualize the relationships between the various objects of a cluster. However, previous methods of subspace, correlation or projected clustering determine an individual subspace for each cluster. In this paper, we demonstrate that it is even much more valuable to find clusters in one common low-dimensional subspace, because then we can study not only the intra-cluster but also the inter-cluster relationships of objects, and the relationships of the whole clusters to each other. We develop the mathematical foundation ORT (Optimal Rigid Transform) to determine an arbitrarily-oriented subspace, suitable for a given cluster structure. Based on ORT, we propose FOSSCLU (Finding the Optimal Sub Space for Clustering), a new iterative clustering algorithm. Our extensive experiments demonstrate that FOSSCLU outperforms the previous methods even in both aspects: clustering and dimensionality reduction.
Starting Page	130
Ending Page	139
File Size	1871093
Page Count	10
File Format	PDF
ISSN	15504786
e-ISBN	9781479943029
DOI	10.1109/ICDM.2014.34
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Covariance matrices Noise Matrix decomposition Principal component analysis Eigenvalues and eigenfunctions Clustering algorithms Transforms Joint Subspace Clustering
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

Document Clustering based on mutual information and PCA subspace

The method of improving estimation Discrete Karhunen-Loeve Transformation using wavelets

Incremental Principal Component Analysis Based On Reduced Subspace Projection

Linear discriminant analysis using sparse matrix transform for face recognition

A New Algorithm Estimating the Mixing Matrix for the Sparse Component Analysis

Principal Component Analysis in Linear Discriminant Analysis Space for Face Recognition

Sparse Subspace Representation for Spectral Document Clustering

Links between PPCA and subspace methods for complete Gaussian density estimation

Quaternion principal component analysis of color images

Finding the Optimal Subspace for Clustering

Similar Documents

Document Clustering based on mutual information and PCA subspace

The method of improving estimation Discrete Karhunen-Loeve Transformation using wavelets

Incremental Principal Component Analysis Based On Reduced Subspace Projection

Linear discriminant analysis using sparse matrix transform for face recognition

A New Algorithm Estimating the Mixing Matrix for the Sparse Component Analysis

Principal Component Analysis in Linear Discriminant Analysis Space for Face Recognition

Sparse Subspace Representation for Spectral Document Clustering

Links between PPCA and subspace methods for complete Gaussian density estimation

Quaternion principal component analysis of color images

Finding the Optimal Subspace for Clustering