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Mining Changes in Sequential Patterns of Data Streams
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|摘要:||Many techniques have been proposed for mining the sequential patterns in data streams. However, most of these techniques do not consider the change characteristics of these patterns over time, or use only a simple static decay function to assign a greater importance to the more recent data in the streams. Nonetheless, the change phenomenon is a kernel issue in data streams, and as time goes by, decision-makers require the ability to identify and predict changes in the sequential patterns over data streams in order to respond to emerging trends in a timely and appropriate manner. Accordingly, this study proposes a new, adaptive model for mining the change in the sequential patterns of data streams. In the proposed approach, the current mining results for the sequential patterns within the data streams are merged with the previous mining results, and the significant change patterns and corresponding degree of change are identified. Then, the degree of change between the current sequential patterns and those in the next mining round is predicted, and the decay rate modified accordingly. Moreover, the corresponding degree of support change of sequential patterns is defined, and a predictor is proposed for predicting changes of pattern types in accordance with their degree of support change. To the best of the authors' knowledge, the model proposed in this study represents the first reported attempt to predict the change phenomenon of sequential patterns in real-world data streams to adapt the sensitivity of the mining model in response. The experimental results confirm the ability of the proposed model to detect the significant change patterns within data streams and to automatically tune the decay rate in accordance with the predicted degree of change in the following mining round and the present state of the data streams. Additionally, the pattern type prediction performance of the proposed model is better than that of two linear regression-based models. As a result, the proposed model provides decision-makers with an effective means of detecting emerging trends within real-world applications such as wireless sensor networks (WSNs) and web logs such that appropriate actions can be formulated in response.|
|Appears in Collections:||資訊科學與工程學系所|
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