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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World Scientific
2012
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oai:scholar.dlu.edu.vn:DLU123456789-305792012-04-30T22:37:06Z Data Mining In Time Series Databases Last , M. Kandel, A. Bunke, H. Econometrics 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? 2012-04-27T08:32:23Z 2012-04-27T08:32:23Z 2004 Book 981-238-290-9 http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/30579 en application/pdf World Scientific |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
Econometrics |
| spellingShingle |
Econometrics Last , M. Kandel, A. Bunke, H. Data Mining In Time Series Databases |
| description |
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? |
| format |
Book |
| author |
Last , M. Kandel, A. Bunke, H. |
| author_facet |
Last , M. Kandel, A. Bunke, H. |
| author_sort |
Last , M. |
| title |
Data Mining In Time Series Databases |
| title_short |
Data Mining In Time Series Databases |
| title_full |
Data Mining In Time Series Databases |
| title_fullStr |
Data Mining In Time Series Databases |
| title_full_unstemmed |
Data Mining In Time Series Databases |
| title_sort |
data mining in time series databases |
| publisher |
World Scientific |
| publishDate |
2012 |
| url |
http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/30579 |
| _version_ |
1757655045157945344 |