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
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 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
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using the fractal dimension to cluster datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering by pattern similarity in large data sets
Proceedings of the 2002 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
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CURLER: finding and visualizing nonlinear correlation clusters
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Linear correlation discovery in databases: a data mining approach
Data & Knowledge Engineering
Comparing Subspace Clusterings
IEEE Transactions on Knowledge and Data Engineering
Deriving quantitative models for correlation clusters
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust information-theoretic clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
RIC: Parameter-free noise-robust clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Outlier-robust clustering using independent components
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Proceedings of the VLDB Endowment
REDUS: finding reducible subspaces in high dimensional data
Proceedings of the 17th ACM conference on Information and knowledge management
ACM Transactions on Knowledge Discovery from Data (TKDD)
SLICE: A Novel Method to Find Local Linear Correlations by Constructing Hyperplanes
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Regional Pattern Discovery in Geo-referenced Datasets Using PCA
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
MACs: Multi-Attribute Co-clusters with High Correlation Information
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
ACM SIGKDD Explorations Newsletter
A fast algorithm for finding correlation clusters in noise data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Detection and visualization of subspace cluster hierarchies
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Clustering very large multi-dimensional datasets with MapReduce
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
INCONCO: interpretable clustering of numerical and categorical objects
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Employing correlation clustering for the identification of piecewise affine models
Proceedings of the 2011 workshop on Knowledge discovery, modeling and simulation
Medical image clustering with domain knowledge constraint
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Generalized projected clustering in high-dimensional data streams
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Feature interaction in subspace clustering using the Choquet integral
Pattern Recognition
A robust seedless algorithm for correlation clustering
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Visualization of Global Correlation Structures in Uncertain 2D Scalar Fields
Computer Graphics Forum
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency clustering across measurement scales
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
Interactive data mining with 3D-parallel-coordinate-trees
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Resolving homonymy with correlation clustering in scholarly digital libraries
Proceedings of the 22nd international conference on World Wide Web companion
Finding multiple global linear correlations in sparse and noisy data sets
Knowledge-Based Systems
Subspace clustering of high-dimensional data: a predictive approach
Data Mining and Knowledge Discovery
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The detection of correlations between different features in a set of feature vectors is a very important data mining task because correlation indicates a dependency between the features or some association of cause and effect between them. This association can be arbitrarily complex, i.e. one or more features might be dependent from a combination of several other features. Well-known methods like the principal components analysis (PCA) can perfectly find correlations which are global, linear, not hidden in a set of noise vectors, and uniform, i.e. the same type of correlation is exhibited in all feature vectors. In many applications such as medical diagnosis, molecular biology, time sequences, or electronic commerce, however, correlations are not global since the dependency between features can be different in different subgroups of the set. In this paper, we propose a method called 4C (Computing Correlation Connected Clusters) to identify local subgroups of the data objects sharing a uniform but arbitrarily complex correlation. Our algorithm is based on a combination of PCA and density-based clustering (DBSCAN). Our method has a determinate result and is robust against noise. A broad comparative evaluation demonstrates the superior performance of 4C over competing methods such as DBSCAN, CLIQUE and ORCLUS.