Hybrid clustering technique of PCA-SM-GHSOM for abnormal and normal classification with quarterly financial ratios of listed TCM company sector. (2016)

• Record Type: Journal Article
• Authors: Yang, Ruicheng
• Is Part Of: International journal of simulation and process modelling. Volume 11:Number 3/4(2016)
• Page Start: 220
• Page End: 228
• Publication Date: 2016
• Subjects: PCA-SM-GHSOM -- cosine similarity -- quarterly financial ratios -- traditional Chinese medicine -- TCM firms -- hybrid clustering -- classification -- growing hierarchical SOM -- GHSOM -- self-organising maps -- principal component analysis -- PCA -- similarity matching -- financial data clustering; Management -- Computer simulation -- Periodicals; Mathematical models -- Periodicals; Operations research -- Periodicals; Simulation methods -- Periodicals; 003.05
• Languages: English
• ISSNs: 1740-2123

Abstract

This paper provides a hybrid technique PCA-SM-GHSOM for clustering the quarterly financial data into normal and abnormal groups. For evaluating the performance of this hybrid method, we give some empirical analysis for the listed traditional Chinese medicine (TCM) companies. Three stages are proposed for the clustering experiment. Firstly, we use the PCA method to reduce the high dimensions of financial ratios into low dimensions. Secondly, we adopt cosine similarity method to select three best matching companies in the same TCM sector, and further get the deviation dataset of the considered company. Finally, we put the deviation dataset into the GHSOM system and get the clustering results for training dataset and testing dataset respectively. Furthermore, we give some comparisons with other different techniques of single GHSOM, PCA-GHSOM and SM-GHSOM, and find that the proposed hybrid technique can improve significantly the accuracy for clustering the financial data into normal and abnormal groups.