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UIST '94 Proceedings of the 7th annual ACM symposium on User interface software and technology
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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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Data Mining and Knowledge Discovery
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
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Informix under CONTROL: Online Query Processing
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ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
Cluster and Calendar Based Visualization of Time Series Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
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In this chapter we describe DataJewel, a new temporal data mining architecture. DataJewel tightly integrates a visualization component, an algorithmic component and a database component. We introduce a new visualization technique called CalendarView as an implementation of the visualization component, and we introduce a data structure that supports temporal mining of large databases. In our architecture, algorithms can be tightly integrated with the visualization component and most existing temporal data mining algorithms can be leveraged by embedding them into DataJewel. This integration is achieved by an interface that is used by both the user and the algorithms to assign colors to events. The user interactively assigns colors to incorporate domain knowledge or to formulate hypotheses. The algorithm assigns colors based on discovered patterns. The same visualization technique is used for displaying both data and patterns to make it more intuitive for the user to identify useful patterns while exploring data interactively or while using algorithms to search for patterns. Our experiments in analyzing several large datasets from the airplane maintenance domain demonstrate the usefulness of our approach and we discuss its applicability to domains like homeland security, market basket analysis and web mining.