Fast discovery of association rules
Advances in knowledge discovery and data mining
DNA visual and analytic data mining
VIS '97 Proceedings of the 8th conference on Visualization '97
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Information visualization in data mining and knowledge discovery
Information visualization in data mining and knowledge discovery
Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Data mining is sometimes treating data consisting of items representing measurements of a single property taken in different time points. In this case data can be understood as a time series of one feature. It is no exception when the clue for evaluation of such data is related to their development trends as observed in several successive time points. From the qualitative point of view one can distinguish 3 basic types of behavior between two neighboring time points: the value of the feature is stable (remains the same), it grows or it falls. This paper is concerned with identification of typical qualitative development patterns as they appear in the windows of given length in the considered time-stamped data and their utilization for specification of interesting subgroups.