Title :
Interactive Evolutionary Computation and density-based clustering for data analysis
Author :
Teh, Chee Siong ; Chen, Chwen Jen
Author_Institution :
Fac. of Cognitive Sci. & Human Dev., Univ. Malaysia Sarawak (UNIMAS), Kota Samarahan
Abstract :
Data clustering is useful in solving many pattern recognition and decision support tasks. This work has empirically demonstrated the effectiveness of a hybrid neural network model for density-based clustering. The cluster regions formed were then evaluated based on visualisation of clustering information on the map. The visual inspection of the map revealed the number of clusters as well as their spatial relationships. By analysing the clustering information in this way, the cluster (or density) structures of the data were obtained. In this paper, a case study of pen-based handwritten digits recognition was chosen to demonstrate how, in this by using the interactive evolutionary computational (IEC), both the computer system and the user work together in the cluster analysis process and subsequently, shown that this approach is suitable for exploratory data analysis.
Keywords :
evolutionary computation; handwritten character recognition; neural nets; pattern clustering; clustering information visualisation; data analysis; data clustering; decision support tasks; density-based clustering; hybrid neural network; interactive evolutionary computation; pattern recognition; pen-based handwritten digits recognition; Data analysis; Embedded computing; Evolutionary computation; Handwriting recognition; Humans; IEC; Intelligent systems; Neural networks; Optimization methods; Pattern recognition;
Conference_Titel :
Intelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4244-1355-3
Electronic_ISBN :
978-1-4244-1356-0
DOI :
10.1109/ICIAS.2007.4658356