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A Model for Predicting Sequential Pattern Changes in Data Streams
Sequential Pattern Mining
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With the development of information technology, stream data grow rapidly in many applications. Unlike traditional data sets, stream data are temporally ordered, fast changing, and massive. Due to its tremendous volume, multiple scans of the entire stream data may not be possible. As a result, traditional sequential pattern mining algorithms are not suitable for data streams. In this thesis, we proposed a sequential pattern mining model for stream data. The proposed model provides functionalities such as mining sequential patterns and predicting pattern changes based on change degree. The experimental results show that the proposed model has high accuracy, about 90%, in terms of predicting pattern changes, and the accuracy is about 5% higher than that of linear regression.
|Appears in Collections:||資訊網路與多媒體研究所|
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