Purpose -The purpose of this paper is to develop a new suit sizing system based on up-dated data, using data mining techniques, to improve the final quality and reduce the waste of fabric. This paper aims to investigate the effect of data reduction on the final fitness of the sizing chart. Design/methodology/approach -Principal component analysis is applied to reduce the sizing variables, non-hierarchical clustering approach is used to segment the heterogeneous population to more homogeneous one, and the aggregate loss of fitness is used to evaluate the resulted sizing chart. Findings -The results show that, when principal component analysis reduces the ten sizing variables to two main components, the final fitness for the resulted sizing chart is the best. These two main components are height and circumference. The hierarchical clustering approach could effectively group all body type to seven clusters. The resulted sizing chart could be used as a reference for suit manufacturers. Practical implications -Due to wide differences in race, nutrition and climate, people who live in different countries have their own body size; also, most of current sizing systems are out-dated, so there is an urgent need to develop a new sizing system. Due to the growing rate of globalization, the final results will be useful for those companies wanting to connect to global business chains. Originality/value -This work introduces the first suit sizing systems, based on data mining, for Iranian males, that has more fitness in comparison to the current sizing chart. The effect of the number of principal components on the final fitness of a sizing system is introduced as an innovative way, to avoid losing useful data during data reduction process with principal component analysis.

Purpose – In shopping, for selecting the appropriate garments, people have to try on multiple garments. This problem is due to lack of a sizing system based on updated anthropometric data and the classification system that introduces the appropriate size from the sizing chart to each person. To solve this problem, as a first study in the literature, a hybrid intelligent classification model as a size recommendation expert system is proposed. The paper aims to discuss these issues. Design/methodology/approach – Three stages for developing a hybrid intelligent classification system based on data clustering and probabilistic neural network (PNN) are proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference for developing a new intelligent classification system by using a PNN. At the last stage, the accuracy of the proposed model is evaluated by using the Iranian male's body type data set. Findings – Experimental results show that the proposed model has a good accuracy and can be used as a size recommendation expert system to specify the right size for the customers. By using the proposed model and designing an interface for it, a decision support system was developed as a size recommendation expert system that was used by an apparel sales store. The results were time saving and more satisfying for the customers by selecting the appropriate apparel size for them. Originality/value – In this paper, as a first study in literature, a hybrid intelligent model for developing a size recommendation expert system based on data clustering and a PNN to enable the salesperson to help the consumer in choosing the right size is proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference to develop a new intelligent classification system by using a PNN. In the last stage, the accuracy of the proposed model is evaluated by using testing data. The proposed model achieved an 87.2 percent accuracy rate that is very promising.

Manufacturing garments has the highest added value in the textile industry. Presently, most sizing systems are outdated, so each country needs to develop a new sizing system for its people. The main goal of this work was to present the new sizing chart for Iranian male suites using an artificial neural network. 10 effective sizing variables for producing suits are determined, and then all different body sizes are clustered using the Kohonen neural network. Aggregate loss is used as a tool to measure fitness of the newly obtained sizing chart. The results have a good coverage on Iranian body types, and could be used as a reference for apparel companies in Iran and other companies which produce suit for Iranian males.

Poly phosphoric acid/ Polyacrilonitrile composite nanofibers (PPA/PAN composite nanofibers) has been fabricated by the electro-spinning of PAN solutions containing different amount of PPA. The prepared nanofibers have been characterized by the using of FT-IR, SEM and XRD techniques

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