The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS

Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.

Identification of Textile Defects Based on GLCM and Neural Networks

In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance in terms of tissue inspection. Based on the co-occurrence matrix and its statistical features, as an approach for defects textile identification in the digital image, TAI can potentially provide an objective and reliable evaluation on the fabric production quality. The goal of most TAI systems is to detect the presence of faults in textiles and accurately locate the position of the defects. The motivation behind the fabric defects identification is to enable an on-line quality control of the weaving process. In this paper, we proposed a method based on texture analysis and neural networks to identify the textile defects. A feature extractor is designed based on Gray Level Co-occurrence Matrix (GLCM). A neural network is used as a classifier to identify the textile defects. The numerical simulation showed that the error recognition rates were 100% for the training and 100%, 91% for the best and worst testing respectively.