Data Mining In Time Series Databases

Traditional data mining methods are designed to deal with “static” databases, i.e. databases where the ordering of records (or other database objects) has nothing to do with the patterns of interest. Though the assumption of order irrelevance may be sufficiently accurate in some applications, th...

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Những tác giả chính: Last , M., Kandel, A., Bunke, H.
Định dạng: Sách
Ngôn ngữ:English
Được phát hành: World Scientific 2012
Những chủ đề:
Truy cập trực tuyến:http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/30579
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Miêu tả
Tóm tắt:Traditional data mining methods are designed to deal with “static” databases, i.e. databases where the ordering of records (or other database objects) has nothing to do with the patterns of interest. Though the assumption of order irrelevance may be sufficiently accurate in some applications, there are certainly many other cases, where sequential information, such as a time-stamp associated with every record, can significantly enhance our knowledge about the mined data. One example is a series of stock values: a specific closing price recorded yesterday has a completely different meaning than the same value a year ago. Since most today’s databases already include temporal data in the form of “date created”, “date modified”, and other time-related fields, the only problem is how to exploit this valuable information to our benefit. In other words, the question we are currently facing is: How to mine time series data?