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This paper presents a temporal pattern mining method for medical data. It modifies the mining algorithms proposed by Batal et al. to incorporate with ranged relations. Experimental results demonstrate that the proposed method could generate frequent patterns with abstracted time ranges embedded in their temporal relations.
Similarity measure is a central problem in time series data mining. Although most approaches to this problem have been developed, with the rapid growth of the amount of data, we believe there is a challenging demand for supporting similarity measure in a fast and accurate way. In this paper, we propose a new time series representation model and a corresponding similarity measure, which is able to...
Clustering is an important branch in the field of data mining as well as statistical analysis and is widely used in exploratory analysis. Many algorithms exist for clustering in the Euclidean space. However, time series clustering introduces new problems, such as inadequate distance measure, inaccurate cluster center description, lack of efficient and accurate clustering techniques. When dealing with...
We propose a new variant of the Correlation-based Feature Selection (CFS) method for coping with longitudinal data – where variables are repeatedly measured across different time points. The proposed CFS variant is evaluated on ten datasets created using data from the English Longitudinal Study of Ageing (ELSA), with different age-related diseases used as the class variables to be predicted. The results...
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