Time series clustering analysis based on wavelet and process neural networks
Guisheng Yin · Dianji yu kongzhi xuebao · 2011
For time series clustering problem,a method based on wavelet and improved self-organization process neural networks(PNN) was proposed.First,original time series data was decomposed by wavelet.Under the principle of reserving clustering characteristics,the signal was reconstructed.And then reconstructed signal fitted into time-varying functions was used as PNN's input.Self-organization PNN was trained by improved competition algorithm.Making use of time-varying input characteristic of PNN,the timing signal characteristics processed by wavelet has been considered adequately in clustering analysis.Network extracts implicit process mode characteristics of function to self organize.The improved competition learning algorithm was given.Finally,clustering result of UCI datasets shows that the proposed approach has an improvement in clustering accuracy,network runtime and convergence speed,at the same time shows good performance in clustering accuracy and clustering speed,can be applied to timing clustering effectively.