|
Title:
|
DATA MINING IN NON-STATIONARY MULTIDIMENSIONAL TIME SERIES USING A RULE SIMILARITY MEASURE |
|
Author(s):
|
Nikolay V. Filipenkov |
|
ISBN:
|
978-972-8924-63-8 |
|
Editors:
|
Hans Weghorn and Ajith P. Abraham |
|
Year:
|
2008 |
|
Edition:
|
Single |
|
Keywords:
|
Data Mining, Time Series, Similarity Measure. |
|
Type:
|
Short Paper |
|
First Page:
|
92 |
|
Last Page:
|
96 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
Time series analysis is a wide area of knowledge that studies processes in their evolution. The classical research in the
area tends to find global laws underlying the behaviour of time series, the contemporary data mining in time series mainly
focuses on the mining of local rules. In plenty of the real world applications it is extremely important for a time series
data mining algorithm to perform correctly on the non-stationary time series. Moreover, in plenty of the real world
applications it should have a continuous change of the data mining algorithms parameters.
In this paper a novel approach for the mining of slowly changing rules is introduced. This approach uses a model of rule
that allows mining both universal and local rules. Moreover, the approach is able to work with non-stationary time series
and performs the mining of the slowly changing rules. The key structure for measuring the speed of change is the rule
similarity measure. It is used as a weight on a rule graph, which shows the shortest path the most slowly changing rule. |
|
|
|
|
|
|
