Data Mining on Nonlinear Temporal Data

Author

Huang, Weitong ; Lu, Mingyu ; Zhao, Zixiang

Author_Institution

Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing

Volume

2

fYear

0

fDate

0-0 0

Firstpage

6068

Lastpage

6072

Abstract

One of the goals of data mining is to discover hidden rules from existing data. Real rules in data differ according to characteristics of the data, and the effect of data mining depends mostly on whether the method selected matches the characteristics of the data. To improve effect of data mining, this paper discusses first correlation of data mining methods and characteristics of data taking temporal data generated from dynamics system as an example, then types of dynamics system since characteristics of data are determined by type of dynamics system and how to determine them from the data. At last we build a neural network to mine the data given the type and parameters of the dynamics system

Keywords

Lyapunov matrix equations; correlation methods; data mining; neural nets; Lyapunov exponent; correlation methods; data mining; dynamics system; hidden rules discovery; neural network; nonlinear temporal data; Blindness; Character generation; Computer science; Data mining; Databases; Educational institutions; IP networks; Neural networks; Open systems; Real time systems; Data mining; Dynamics; Lyapunov exponent; Neural network; Phase space; Temporal data;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714246

Filename

1714246