Clustering with feature order preferences

Authors:
Jun Sun;Wenbo Zhao;Jiangwei Xue;Zhiyong Shen;Yidong Shen
Affiliations:
(Correspd.) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China and Graduate University, Chinese Academy of Sciences, Beijing, China;Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA;Department of Mathematics, The Pennsylvania State University, Pennsylvania, PA, USA;State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China and Graduate University, Chinese Academy of Sciences, Beijing, China;State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
Venue:
Intelligent Data Analysis - Artificial Intelligence
Year:
2010

Citing 23
Cited 1

Algorithms for clustering data

Algorithms for clustering data
Data clustering: a review

ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Why so many clustering algorithms: a position paper

ACM SIGKDD Explorations Newsletter
COOLCAT: an entropy-based algorithm for categorical clustering

Proceedings of the eleventh international conference on Information and knowledge management
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

Data Mining and Knowledge Discovery
Knowledge Acquisition Via Incremental Conceptual Clustering

Machine Learning
Feature Weighting in k-Means Clustering

Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

The Journal of Machine Learning Research
Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering

Machine Learning
Convex Optimization

Convex Optimization
A probabilistic framework for semi-supervised clustering

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank using gradient descent

ICML '05 Proceedings of the 22nd international conference on Machine learning
Semi-supervised graph clustering: a kernel approach

ICML '05 Proceedings of the 22nd international conference on Machine learning
Clustering with Bregman Divergences

The Journal of Machine Learning Research
Locally adaptive metrics for clustering high dimensional data

Data Mining and Knowledge Discovery
Regularized clustering for documents

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
BoostCluster: boosting clustering by pairwise constraints

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing semi-supervised clustering: a feature projection perspective

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Consensus Clusterings

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Kernel regression with order preferences

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
An axiomatization of partition entropy

IEEE Transactions on Information Theory
On Defining Partition Entropy by Inequalities

IEEE Transactions on Information Theory

A general non-local denoising model using multi-kernel-induced measures

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a clustering algorithm that effectively utilizes feature order preferences, which have the form that feature s is more important than feature t. Our clustering formulation aims to incorporate feature order preferences into prototype-based clustering. The derived algorithm automatically learns distortion measures parameterized by feature weights which will respect the feature order preferences as much as possible. Our method allows the use of a broad range of distortion measures such as Bregman divergences. Moreover, even when generalized entropy is used in the regularization term, the subproblem of learning the feature weights is still a convex programming problem. Empirical results on some datasets demonstrate the effectiveness and potential of our method.