Algorithms for clustering data
Algorithms for clustering data
Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Voting-Merging: An Ensemble Method for Clustering
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Analysis of Consensus Partition in Cluster Ensemble
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A scalable framework for cluster ensembles
Pattern Recognition
Heterogeneous source consensus learning via decision propagation and negotiation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting context analysis for combining multiple entity resolution systems
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
How to Control Clustering Results? Flexible Clustering Aggregation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Comparing clustering and metaclustering algorithms
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Positional and confidence voting-based consensus functions for fuzzy cluster ensembles
Fuzzy Sets and Systems
Integrating community matching and outlier detection for mining evolutionary community outliers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
Combining multiple clusterings arises in various important data mining scenarios. However, finding a consensus clustering from multiple clusterings is a challenging task because there is no explicit correspondence between the classes from different clusterings. We present a new framework based on soft correspondence to directly address the correspondence problem in combining multiple clusterings. Under this framework, we propose a novel algorithm that iteratively computes the consensus clustering and correspondence matrices using multiplicative updating rules. This algorithm provides a final consensus clustering as well as correspondence matrices that gives intuitive interpretation of the relations between the consensus clustering and each clustering from clustering ensembles. Extensive experimental evaluations also demonstrate the effectiveness and potential of this framework as well as the algorithm for discovering a consensus clustering from multiple clusterings.