Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Computing Clusters of Correlation Connected objects
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
CURLER: finding and visualizing nonlinear correlation clusters
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Data & Knowledge Engineering
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
A tutorial on spectral clustering
Statistics and Computing
Information and Complexity in Statistical Modeling
Information and Complexity in Statistical Modeling
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
INCONCO: interpretable clustering of numerical and categorical objects
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering mixed type attributes in large dataset
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
Integrative parameter-free clustering of data with mixed type attributes
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Coupled attribute analysis on numerical data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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How to automatically spot the major trends in large amounts of heterogeneous data? Clustering can help. However, most existing techniques suffer from one or more of the following drawbacks: 1) Many techniques support only one particular data type, most commonly numerical attributes. 2) Other techniques do not support attribute dependencies which are prevalent in real data. 3) Some approaches require input parameters which are difficult to estimate. 4) Most clustering approaches lack in interpretability. To address these challenges, we present the algorithm Scenic for dependency clustering across measurement scales. Our approach seamlessly integrates heterogenous data types measured at different scales, most importantly continuous numerical and discrete categorical data. Scenic clusters by arranging objects and attributes in a cluster-specific low-dimensional space. The embedding serves as a compact cluster model allowing to reconstruct the original heterogenous attributes with high accuracy. Thereby embedding reveals the major cluster-specific mixed-type attribute dependencies. Following the Minimum Description Length (MDL) principle, the cluster-specific embedding serves as a codebook for effective data compression. This compression-based view automatically balances goodness-of-fit and model complexity, making input parameters redundant. Finally, the embedding serves as a visualization enhancing the interpretability of the clustering result. Extensive experiments demonstrate the benefits of Scenic.