Fuzzy time series prediction using hierarchical clustering algorithms

[Display omitted] In many cases, the k-means clustering algorithm has been most frequently used to the field of data mining, fuzzy control systems and prediction since it was designed in simple procedures and excellent ability of classification. However, it sometimes brought about the failed results...

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Vydáno v:	Expert systems with applications Ročník 38; číslo 4; s. 4312 - 4325
Hlavní autoři:	Bang, Young-Keun, Lee, Chul-Heui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.04.2011
Témata:	Cross-correlation clustering algorithm Hierarchical clustering algorithms Multiple model fuzzy predictors Non-linear time series Non-linear time series Cross-correlation clustering algorithm Multiple model fuzzy predictors Hierarchical clustering algorithms
ISSN:	0957-4174, 1873-6793
On-line přístup:	Získat plný text
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Popis
Shrnutí:	[Display omitted] In many cases, the k-means clustering algorithm has been most frequently used to the field of data mining, fuzzy control systems and prediction since it was designed in simple procedures and excellent ability of classification. However, it sometimes brought about the failed results for non-linear data by classification behavior caused by just considering the statistical characteristics of non-linear data such as distances between data. To overcome the problems above, this paper proposes a new clustering algorithm of which the structure hierarchically classifies non-linear data. The proposed hierarchical classification technique consists of two levels, called upper clusters and lower fuzzy sets, using the cross-correlation clustering algorithm combined with the k-means clustering algorithm (HCKA), and it was able to improve classification accuracy. In addition, this paper constructs multiple model fuzzy predictors (MMFPs) corresponding to difference data of original time series, which was able to reflect the various characteristics of the time series to the proposed system. Simulation results show that the proposed system was effective and useful for modeling and predicting non-linear time series.
ISSN:	0957-4174 1873-6793
DOI:	10.1016/j.eswa.2010.09.100