TITLE:
Clustering-Inverse: A Generalized Model for Pattern-Based Time Series Segmentation
AUTHORS:
Zhaohong Deng, Fu-Lai Chung, Shitong Wang
KEYWORDS:
Pattern-Based Time Series Segmentation, Clustering-Inverse, Dynamic Time Warping, Perceptually Important Points, Evolution Computation, Particle Swarm Optimization, Genetic Algorithm
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.3 No.1,
February
24,
2011
ABSTRACT: Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.