{'fraction_non_alphanumeric': 0.02476958525345622, 'fraction_numerical': 0.004608294930875576, 'mean_word_length': 5.270758122743683, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A comparative study on application of data mining technique in human shape clustering: Principal component analysis vs. Factor analysis

Traditional human shape classification usually adopted some key measurements, leading several problems to product ergonomic design. Multivariate analysis is able to supplement the disadvantages of traditional method. Among methods of multivariate analysis, Principal component analysis (PCA) and Factor analysis (FA) have enjoyed widespread popularity. Though both of them are to reduce the dimensions of variances in the sample, there are differences between PCA and FA worth further investigation. The purpose of the paper is to demonstrate the differences between PCA and FA by analyzing the multivariate anthropometric data. K-means cluster analysis was developed to divide samples into groups with homogenous characteristics according to the PCA scores (or FA scores). ANOVA (analysis of variance) was adopted to compare the dimensions in corresponding clusters between PCA and FA. For all the dimensions, the p-value equals to 0.000, indicating there is significant difference for the samples between PCA and FA at the significance level of 0.005. Finally, the regression models of the reference dimensions based on the key dimensions, i.e., stature and waist girth, were investigated for the ease of utilization the FA (or PCA) results into applications such as building a family of digital manikin. In conclusion, the techniques have similarities and differences, and should not be abused. PCA analyzes all variance of the data set, while FA analyzes only common variances. A priori decision on the techniques depends on the domain expertise, and the statistic characteristics of the sample.