A fast algorithm for mining similar trends in trend-sequence databases

Author

Guo, Si-yu ; Wu, Tie-Jun

Author_Institution

Nat. Key Lab. of Ind. Control, Zhejiang Univ., Hangzhou, China

Volume

3

fYear

2002

fDate

2002

Firstpage

1362

Abstract

Mining time series data is a novel and important issue in the field of data mining. An approach of retrieving interesting information in the time series is proposed. This approach converts time series into symbolized, "trend sequences", and then considers the task of finding the trends similar to given query trend in the trend databases. To solve the problem, definitions of trend are given, consequently followed by definitions and properties of trend distribution. An index based on trend distribution is constructed and applied for filtering out dissimilar candidate trends to accelerate searching. Experiments show that this index is effective to accelerate the searching of similar trends, under the condition of small trend indicator set and trends being within "low frequency" band.

Keywords

data mining; database management systems; time series; dissimilar candidate trends; fast algorithm; interesting information retrieval; similar trend mining; symbolized trend sequences; time series data mining; trend distribution; trend-sequence databases; Acceleration; Data analysis; Data mining; Databases; Filtering; Frequency; Indexes; Industrial control; Information retrieval; Laboratories;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on

Print_ISBN

0-7803-7508-4

Type

conf

DOI

10.1109/ICMLC.2002.1167428

Filename

1167428