Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Mining rank-correlated sets of numerical attributes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
A tutorial on spectral clustering
Statistics and Computing
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Instance-level semisupervised multiple instance learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Word co-occurrence features for text classification
Information Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled nominal similarity in unsupervised learning
Proceedings of the 20th ACM international conference on Information and knowledge management
Extending Attribute Information for Small Data Set Classification
IEEE Transactions on Knowledge and Data Engineering
Text document clustering using global term context vectors
Knowledge and Information Systems
Discerning linkage-based algorithms among hierarchical clustering methods
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Revisiting numerical pattern mining with formal concept analysis
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Discovering deformable motifs in continuous time series data
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Relation adaptation: learning to extract novel relations with minimum supervision
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Coupled Behavior Analysis with Applications
IEEE Transactions on Knowledge and Data Engineering
Dependency clustering across measurement scales
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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The usual representation of quantitative data is to formalize it as an information table, which assumes the independence of attributes. In real-world data, attributes are more or less interacted and coupled via explicit or implicit relationships. Limited research has been conducted on analyzing such attribute interactions, which only describe a local picture of attribute couplings in an implicit way. This paper proposes a framework of the coupled attribute analysis to capture the global dependency of continuous attributes. Such global couplings integrate the intra-coupled interaction within an attribute (i.e. the correlations between attributes and their own powers) and inter-coupled interaction among different attributes (i.e. the correlations between attributes and the powers of others) to form a coupled representation for numerical objects by the Taylor-like expansion. This work makes one step forward towards explicitly addressing the global interactions of continuous attributes, verified by the applications in data structure analysis, data clustering, and data classification. Substantial experiments on 13 UCI data sets demonstrate that the coupled representation can effectively capture the global couplings of attributes and outperforms the traditional way, supported by statistical analysis.