Self-organizing feature map for cluster analysis in multi-disease diagnosis

Authors:
Ke Zhang;Yi Chai;Simon X. Yang
Affiliations:
Automation College, Chongqing University, Chongqing 400030, China and School of Engineering, University of Guelph, Guelph, Ontario, Canada N1G 2W1;State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Automation, Chongqing University, Chongqing 400030, China;School of Engineering, University of Guelph, Guelph, Ontario, Canada N1G 2W1
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Aiming at the multi-disease diagnosis, a self-organizing map (SOM) is developed. In this paper the tomato disease features are extracted and a mapping relationship between the diseases and the features is created. The inaccurate clustering of traditional SOM algorithm is analyzed. According to the analysis, Euclidean distance is taken as the main discrimination, and the adjacent-searching algorithm is optimized. Using the optimized algorithm, the cluster results of input samples are obtained, features of diseases are mapped, and a multi-disease diagnosis model is developed. The proposed SOM-based model has two layers. The feature array of diseases can be accurately and rapidly sorted and clustered using this model. This model can achieve an accurate diagnosis of multi-diseases. The simulation results show that the proposed model performs well and the proposed multi-disease diagnosis is effective.