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| Content Provider | ACM Digital Library |
|---|---|
| Author | Motoyoshi, Masahiro Watanabe, Kohei Miura, Takao |
| Abstract | In this investigation, we discuss how to mine Temporal Class Schemes to model a collection of time series data. From the viewpoint of temporal data mining, this problem can be seen as discretizing time series data or aggregating them. Also this can be considered as screening (or noise filtering). From the viewpoint of temporal databases, the issue is how we represent the data and how we can obtain intensional aspects as temporal schemes. In other words, we discuss scheme discovery for temporal data. Given a collection of temporal objects along with time axis (called log), we examine the data and we introduce a notion of temporal frequent classes to describe them. As the main results of this investigation, we can show that there exists one and only one interval decomposition and the temporal classes related to them. Also we give experimental results that prove the feasibility to time series data. |
| Starting Page | 493 |
| Ending Page | 498 |
| Page Count | 6 |
| File Format | |
| ISBN | 1581134924 |
| DOI | 10.1145/584792.584873 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2002-11-04 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Scheme discovery Temporal scheme Data mining Time series data |
| Content Type | Text |
| Resource Type | Article |
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