Pattern-Miner: integrated management and mining over data mining models
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Using Global Optimization to Explore Multiple Solutions of Clustering Problems
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Robust Clustering by Aggregation and Intersection Methods
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Monitoring Patterns through an Integrated Management and Mining Tool
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Change analysis in spatial datasets by interestingness comparison
SIGSPATIAL Special
Interactive Visualization Tools for Meta-Clustering
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Metaclustering and Consensus Algorithms for Interactive Data Analysis and Validation
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Multiple data structure discovery through global optimisation, meta clustering and consensus methods
International Journal of Knowledge Engineering and Soft Data Paradigms
Proceedings of the 18th ACM conference on Information and knowledge management
Global optimization, meta clustering and consensus clustering for class prediction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Application notes: data mining in cancer research
IEEE Computational Intelligence Magazine
Towards subjectifying text clustering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning multiple nonredundant clusterings
ACM Transactions on Knowledge Discovery from Data (TKDD)
Data Mining and Knowledge Discovery
A polygon-based methodology for mining related spatial datasets
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
Which clustering do you want? inducing your ideal clustering with minimal feedback
Journal of Artificial Intelligence Research
Kernel based K-medoids for clustering data with uncertainty
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Designing an ensemble classifier over subspace classifiers using iterative convergence routine
Proceedings of the 20th ACM international conference on Information and knowledge management
Model-based multidimensional clustering of categorical data
Artificial Intelligence
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
Projective clustering ensembles
Data Mining and Knowledge Discovery
How to "alternatize" a clustering algorithm
Data Mining and Knowledge Discovery
An efficient and scalable family of algorithms for combining clusterings
Engineering Applications of Artificial Intelligence
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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Clustering is ill-defined. Unlike supervised learning where labels lead to crisp performance criteria such as accuracy and squared error, clustering quality depends on how the clusters will be used. Devising clustering criteria that capture what users need is difficult. Most clustering algorithms search for optimal clusterings based on a pre-specified clustering criterion. Our approach differs. We search for many alternate clusterings of the data, and then allow users to select the clustering(s) that best fit their needs. Meta clustering first finds a variety of clusterings and then clusters this diverse set of clusterings so that users must only examine a small number of qualitatively different clusterings. We present methods for automatically generating a diverse set of alternate clusterings, as well as methods for grouping clusterings into meta clusters. We evaluate meta clustering on four test problems and two case studies. Surprisingly, clusterings that would be of most interest to users often are not very compact clusterings.