A distance and angle similarity measure method
Journal of the American Society for Information Science
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Currency exchange rate forecasting from news headlines
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
News Sensitive Stock Trend Prediction
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
In & out zooming on time-aware user/tag clusters
Journal of Intelligent Information Systems
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A huge amount of data is circulated and collected every day on a regular time basis. Given a pair of such datasets, it might be possible to reveal hidden dependencies between them since the presence of the one dataset elements may influence the elements of the other dataset and vice versa. Furthermore, the impact of these relations may last during a period instead of the time point of their co-occurrence. Mining such relations under those assumptions is a challenging problem. In this paper, we study two time-related datasets whose elements are bilaterally affected over time. We employ a co-clustering approach to identify groups of similar elements on the basis of two distinct criteria: the direction and duration of their impact. The proposed approach is evaluated using time-related news and stock's market real datasets.