Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovering associations with numeric variables
Proceedings of the seventh 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
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
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
Computing Clusters of Correlation Connected objects
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Density Connected Clustering with Local Subspace Preferences
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Quantitative Association Rules Based on Half-Spaces: An Optimization Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Cluster Cores-Based Clustering for High Dimensional Data
ICDM '04 Proceedings of the Fourth 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
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the VLDB Endowment
Finding regional co-location patterns for sets of continuous variables in spatial datasets
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
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
ACM SIGKDD Explorations Newsletter
Minimum variance associations: discovering relationships in numerical data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in 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
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey on unsupervised outlier detection in high-dimensional numerical data
Statistical Analysis and Data Mining
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
Finding multiple global linear correlations in sparse and noisy data sets
Knowledge-Based Systems
Hi-index | 0.00 |
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a common arbitrarily oriented hyperplane of arbitrary dimensionality. Several algorithms for this task have been proposed recently. However, all algorithms only compute the partitioning of the data into clusters. This is only a first step in the pipeline of advanced data analysis and system modelling. The second (post-clustering) step of deriving a quantitative model for each correlation cluster has not been addressed so far. In this paper, we describe an original approach to handle this second step. We introduce a general method that can extract quantitative information on the linear dependencies within a correlation clustering. Our concepts are independent of the clustering model and can thus be applied as a post-processing step to any correlation clustering algorithm. Furthermore, we show how these quantitative models can be used to predict the probability distribution that an object is created by these models. Our broad experimental evaluation demonstrates the beneficial impact of our method on several applications of significant practical importance.