CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
Ontologies Improve Text Document Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Supervised clustering with support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
Distributed Data Mining in Peer-to-Peer Networks
IEEE Internet Computing
Aspect-Based Tagging for Collaborative Media Organization
From Web to Social Web: Discovering and Deploying User and Content Profiles
Online discovery and maintenance of time series motifs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Nemoz: a distributed framework for collaborative media organization
Ubiquitous knowledge discovery
Nemoz: a distributed framework for collaborative media organization
Ubiquitous knowledge discovery
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Personal media collections are structured in very different ways by different users. Their support by standard clustering algorithms is not sufficient. First, users have their personal preferences which they hardly can express by a formal objective function. Instead, they might want to select among a set of proposed clusterings. Second, users most often do not want hand-made partial structures be overwritten by an automatic clustering. Third, given clusterings of others should not be ignored but used to enhance the own structure. In contrast to other cluster ensemble methods or distributed clustering, a global model (consensus) is not the aim. Hence, we investigate a new learning task, namely learning localized alternative cluster ensembles, where a set of given clusterings is taken into account and a set of proposed clusterings is delivered. This paper proposes an algorithm for solving the new task together with a method for evaluation.