109 search results - page 2 / 22 » Rule Discovery from Time Series |
ICDM
2003
IEEE
2003
IEEE
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
13 years 10 months ago
Given the recent explosion of interest in streaming data and online algorithms, clustering of time series subsequences, extracted via a sliding window, has received much attention...
IEAAIE
2009
Springer
13 years 11 months ago
2009
Springer
Change Point Discovery is a basic algorithm needed in many time series mining applications including rule discovery, motif discovery, casual analysis, etc. Several techniques for c...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
The detection of repeated subsequences, time series motifs, is a problem which has been shown to have great utility for several higher-level data mining algorithms, including clas...
JIIS
2000
13 years 4 months ago
2000
Knowledge discovery from data sets can be extensively automated by using data mining software tools. Techniques for mining series of interval events, however, have not been conside...
FQAS
2004
Springer
13 years 8 months ago
2004
Springer
The discovery of frequently occurring patterns in a time series could be important in several application contexts. As an example, the analysis of frequent patterns in biomedical ...