Visualizing time series state changes with prototype based clustering
作者： Markus Pylvänen , Sami Äyrämö , Tommi Kärkkäinen
DOI: 10.1007/978-3-642-04921-7_63
关键词: Adaptive resonance theory 、 Series (mathematics) 、 Cluster analysis 、 Real-time computing 、 Condition monitoring 、 Time series 、 Process (computing) 、 Data mining 、 Visualization 、 Computer science 、 State (computer science)
摘要: Modern process and condition monitoring systems produce a huge amount of data which is hard to analyze manually. Previous analyzing techniques disregard time information concentrate only for the indentification normal abnormal operational states. We present new method visualizing states overall order transitions between them. This implemented visualization tool helps user see development allowing find causes behaviour. In end tested in practice with real series collected from gear unit.
springer.com
sci-hub.st 参考文章(23)
Georges G. Grinstein, Patrick E. Hoffman, A survey of visualizations for high-dimensional data mining Information visualization in data mining and knowledge discovery. pp. 47- 82 ,(2001)
Mikko Heikkinen, Ari Kettunen, Eero Niemitalo, Reijo Kuivalainen, Yrjö Hiltunen, SOM-Based Method for Process State Monitoring and Optimization in Fluidized Bed Energy Plant Artificial Neural Networks: Biological Inspirations – ICANN 2005. pp. 409- 414 ,(2005) , 10.1007/11550822_64
Miquel Sànchez, Ulises Cortés, Javier Béjar, Joan De Grácia, Javier Lafuente, Manel Poch, Concept Formation in WWTP by Means of Classification Techniques: ACompared Study Applied Intelligence. ,vol. 7, pp. 147- 165 ,(1997) , 10.1023/A:1008202113300
E. W. Forgy, Cluster analysis of multivariate data : efficiency versus interpretability of classifications Biometrics. ,vol. 21, pp. 768- 769 ,(1965)
Esa Alhoniemi, Jaakko Hollmén, Olli Simula, Juha Vesanto, Process Monitoring and Modeling Using the Self-Organizing Map Computer-Aided Engineering. ,vol. 6, pp. 3- 14 ,(1999) , 10.3233/ICA-1999-6102
C. McGreavy, Xue Zhang Wang, Data Mining and Knowledge Discovery for Process Monitoring and Control ,(1999)
Zhexue Huang, Tao Lin, A Visual Method of Cluster Validation with Fastmap pacific-asia conference on knowledge discovery and data mining. pp. 153- 164 ,(2000) , 10.1007/3-540-45571-X_18
Hans-Peter Kriegel, Martin Ester, Jörg Sander, Xiaowei Xu, A density-based algorithm for discovering clusters in large spatial Databases with Noise knowledge discovery and data mining. pp. 226- 231 ,(1996)
Richard C. Dubes, Anil K. Jain, Algorithms for clustering data ,(1988)
Samuli Visuri, Visa Koivunen, Hannu Oja, Sign and rank covariance matrices Journal of Statistical Planning and Inference. ,vol. 91, pp. 557- 575 ,(2000) , 10.1016/S0378-3758(00)00199-3