Browsing by Author "SHENG, Chang"

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    Discovering Spatial Interaction Patterns
    (2007-06-29) SHENG, Chang; HSU, Wynne; LEE, Mong Li
    Advances in sensing and satellite technologies and the growth of Internet have resulted in a vast amount of spatial data that are easily accessible. Extracting useful knowledge from these data has remained an important and challenging task. Among the various spatial analysis tasks, finding interaction among spatial features is one of the most important problem. Existing works typically adopt a grid-like approach to transform the continuous space to a discrete space. This may lead to some meaningful knowledge being missed. In this paper, we propose to model the spatial features in a continuous space through the use of influence functions. For each feature type, we build an influence map that captures the distribution of the feature instances. Superimposing the influence maps allows the interaction of the feature types to be quickly determined. Experiments on both synthetic and real world datasets indicate that the proposed approach is scalable and is able to discover patterns that have ...
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    Efficient Mining of Dense Periodic Patterns in Time Series
    (2005-10-31) SHENG, Chang; HSU, Wynne; LEE, Mong Li
    Existing techniques to mine periodic patterns in time series data are focused on discovering full-cycle periodic patterns from an entire time series. However, many useful partial periodic patterns are hidden in long and complex time series data. In this paper, we aim to discover the partial periodicity in local segments of the time series data. We introduce the notion of character density to partition the time series into variable-length fragments and to determine the lower bound of each character's period. We propose a novel algorithm, called DPMiner, to .nd the dense periodic patterns in time series data. The algorithm makes use of an Apriori-like property to prune the search space. Experimental results on both synthetic and real-life datasets demonstrate that the proposed algorithm is effective and ef.cient to reveal interesting dense periodic patterns.