Interactive Evolutionary Computation and Density- based Clustering for Data Analysis

Teh, Chee Siong and Chen, Chwen Jen (2007) Interactive Evolutionary Computation and Density- based Clustering for Data Analysis. In: Proceedings of International Conference on Intelligent & Advance Systems (ICIAS 2007).

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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.

Item Type: Proceeding (Paper)
Uncontrolled Keywords: Data clustering, interactive evolutionary computational (IEC), research, Universiti Malaysia Sarawak, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education
Subjects: L Education > L Education (General)
T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: George Gerrie
Date Deposited: 05 Jan 2016 03:36
Last Modified: 05 Jan 2016 03:36

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