Spelling checkers,spelling correctors and the misspellings of poor spellers
Information Processing and Management: an International Journal
Introduction to optimization methods and their application in statistics
Introduction to optimization methods and their application in statistics
Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Reinterpreting the Category Utility Function
Machine Learning
Distributed clustering using collective principal component analysis
Knowledge and Information Systems
The Theory of Computation
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Style mining of electronic messages for multiple authorship discrimination: first results
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic text categorization in terms of genre and author
Computational Linguistics
Document clustering via adaptive subspace iteration
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Music artist style identification by semi-supervised learning from both lyrics and content
Proceedings of the 12th annual ACM international conference on Multimedia
Algorithms for clustering high dimensional and distributed data
Intelligent Data Analysis
Comparing Non-parametric Ensemble Methods for Document Clustering
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
On combining multiple clusterings: an overview and a new perspective
Applied Intelligence
The effect of cooling functions on ensemble clustering using simulated annealing
Intelligent Data Analysis
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Many problems can be reduced to the problem of combining multiple clusterings. In this paper, we first summarize different application scenarios of combining multiple clusterings and provide a new perspective of viewing the problem as a categorical clustering problem. We then show the connections between various consensus and clustering criteria and discuss the complexity results of the problem. Finally we propose a new method to determine the final clustering. Experiments on kinship terms and clustering popular music from heterogeneous feature sets show the effectiveness of combining multiple clusterings.