Algorithms for clustering data
Algorithms for clustering data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
Improving Performance of Similarity-Based Clustering by Feature Weight Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Locality preserving clustering for image database
Proceedings of the 12th annual ACM international conference on Multimedia
A non-linear dimensionality-reduction technique for fast similarity search in large databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
K-means clustering versus validation measures: a data distribution perspective
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A clustering framework based on subjective and objective validity criteria
ACM Transactions on Knowledge Discovery from Data (TKDD)
Leveraging user query log: toward improving image data clustering
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Identifying and generating easy sets of constraints for clustering
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Measuring constraint-set utility for partitional clustering algorithms
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
ACM Transactions on Knowledge Discovery from Data (TKDD)
A fast divisive clustering algorithm using an improved discrete particle swarm optimizer
Pattern Recognition Letters
A probabilistic majorclust variant for the clustering of near-homogeneous graphs
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Clustering complex data with group-dependent feature selection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Interactive feature selection for document clustering
Proceedings of the 2011 ACM Symposium on Applied Computing
Clustering very large multi-dimensional datasets with MapReduce
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised document clustering with dual supervision through seeding
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Enhancing semi-supervised document clustering with feature supervision
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A unified framework for document clustering with dual supervision
ACM SIGAPP Applied Computing Review
Automated feature weighting in naive bayes for high-dimensional data classification
Proceedings of the 21st ACM international conference on Information and knowledge management
Clustering Based on Independent Component
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Probability-one homotopy maps for tracking constrained clustering solutions
Proceedings of the High Performance Computing Symposium
QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases
Information Sciences: an International Journal
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Data clustering is a difficult problem due to the complex and heterogeneous natures of multidimensional data. To improve clustering accuracy, we propose a scheme to capture the local correlation structures: associate each cluster with an independent weighting vector and embed it in the subspace spanned by an adaptive combination of the dimensions. Our clustering algorithm takes advantage of the known pairwise instance-level constraints. The data points in the constraint set are divided into groups through inference; and each group is assigned to the feasible cluster which minimizes the sum of squared distances between all the points in the group and the corresponding centroid. Our theoretical analysis shows that the probability of points being assigned to the correct clusters is much higher by the new algorithm, compared to the conventional methods. This is confirmed by our experimental results, indicating that our design indeed produces clusters which are closer to the ground truth than clusters created by the current state-of-the-art algorithms.