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
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
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
SCHISM: A New Approach for Interesting Subspace Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Iterative Projected Clustering by Subspace Mining
IEEE Transactions on Knowledge and Data Engineering
A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Simultaneous Unsupervised Learning of Disparate Clusterings
Statistical Analysis and 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
Detection of orthogonal concepts in subspaces of high dimensional data
Proceedings of the 18th ACM conference on Information and knowledge management
What's PMML and what's new in PMML 4.0?
ACM SIGKDD Explorations Newsletter
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
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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In today's applications we face the challenge of analyzing databases with many attributes per object. For these high dimensional data it is known that traditional clustering algorithms fail to detect meaningful patterns: mining the full-space is futile. As a solution subspace clustering techniques were introduced. They analyze arbitrary subspace projections of the data to detect clustering structures. Recently, public available mining software integrates subspace clustering as a novel mining paradigm and sets the stage for its wide applicability. Though, a common standard to describe, exchange and process the subspace clustering results is still missing, which hinders the application in practice. In this work, we propose an extension of the PMML standard to describe mining models resulting from subspace clustering methods. Thus, we bridge the gap between the different tools and realize a common baseline the user can rely on. Our extension considers the various aspects subspace clustering models have to cope with, going beyond the ones of traditional clustering. We will integrate this novel PMML extension in the next version of our OpenSubspace toolkit.