Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Frequent-Pattern based Iterative Projected Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Comparing Subspace Clusterings
IEEE Transactions on Knowledge and Data Engineering
P3C: A Robust Projected Clustering Algorithm
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The Chosen Few: On Identifying Valuable Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
DUSC: Dimensionality Unbiased Subspace Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Adapting the right measures for K-means clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Subspace and projected clustering: experimental evaluation and analysis
Knowledge and Information Systems
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
Understanding of Internal Clustering Validation Measures
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Multi-view clustering using mixture models in subspace projections
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining coherent subgraphs in multi-layer graphs with edge labels
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining of temporal coherent subspace clusters in multivariate time series databases
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Projective clustering ensembles
Data Mining and Knowledge Discovery
RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Stochastic subspace search for top-k multi-view clustering
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
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Knowledge discovery in databases requires not only development of novel mining techniques but also fair and comparable quality assessment based on objective evaluation measures. Especially in young research areas where no common measures are available, researchers are unable to provide a fair evaluation. Typically, publications glorify the high quality of one approach only justified by an arbitrary evaluation measure. However, such conclusions can only be drawn if the evaluation measures themselves are fully understood. In this paper, we provide the basis for systematic evaluation in the emerging research area of subspace clustering. We formalize general quality criteria for subspace clustering measures not yet addressed in the literature. We compare the existing external evaluation methods based on these criteria and pinpoint limitations. We propose a novel external evaluation measure which meets the requirements in form of quality properties. In thorough experiments we empirically show characteristic properties of evaluation measures. Overall, we provide a set of evaluation measures that fulfill the general quality criteria as recommendation for future evaluations. All measures and datasets are provided on our website and are integrated in our evaluation framework.