Algorithms for clustering data
Algorithms for clustering data
Machine Learning
Self-organizing maps
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Classifier Combinations: Implementations and Theoretical Issues
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Analysis of Consensus Partition in Cluster Ensemble
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining partitions by probabilistic label aggregation
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Text in Videos Using Fuzzy Clustering Ensembles
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Moderate diversity for better cluster ensembles
Information Fusion
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
Fuzzy Clustering Ensemble Based on Dual Boosting
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed and Parallelled EM Algorithm for Distributed Cluster Ensemble
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 02
Incremental construction of classifier and discriminant ensembles
Information Sciences: an International Journal
Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
Artificial Intelligence in Medicine
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
K-means clustering versus validation measures: a data-distribution perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning assignment order of instances for the constrained K-means clustering algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized competitive learning of Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A dynamic classifier ensemble selection approach for noise data
Information Sciences: an International Journal
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
Enhanced clustering of biomedical documents using ensemble non-negative matrix factorization
Information Sciences: an International Journal
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
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This paper investigates the problem of integrating multiple structures which are extracted from different sets of data points into a single unified structure. We first propose a new generalized concept called structure ensemble for the fusion of multiple structures. Unlike traditional cluster ensemble approaches the main objective of which is to align individual labels obtained from different clustering solutions, the structure ensemble approach focuses on how to unify the structures obtained from different data sources. Based on this framework, a new structure ensemble approach called the probabilistic bagging based structure ensemble approach (BSEA) is designed, which integrates the bagging technique, the force based self-organizing map (FBSOM) and the normalized cut algorithm into the proposed framework. BSEA views structures obtained from different datasets generated by the bagging technique as nodes in a graph, and adopts graph theory to find the most representative structure. In addition, the force based self-organizing map (FBSOM), which is a generalized form of SOM, is proposed to serve as the basic clustering algorithm in the structure ensemble framework. Finally, a new external index called correlation index (CI), which considers the correlation relationship of both the similarity and dissimilarity between the predicted solution and the true solution, is proposed to evaluate the performance of BSEA. The experiments show that (i) The performance of BSEA outperforms most of the state-of-the-art clustering approaches, and (ii) BSEA performs well on datasets from the UCI repository and real cancer gene expression profiles.