闪火条件下防火服装热收缩形变的数据挖掘

随着暖体假人、燃烧假人、三维扫描仪等服装设备的应用，服装工程的大样本实验产生了大量数据，采用传统的数据分析方法未能发挥实验中大样本量的优势。本研究利用 Clementine 软件，选取了合适的分析方法，对防火服装的形变等有关数据进行了数据挖掘的尝试。首先通过变量重要性分析，研究了热收缩的重要影响因素，然后根据决策树与神经网络的变量重要性排序，提取了影响热收缩的关键因素，并进一步通过对热收缩及关键影响因素的聚类分析，探索了防火服不同部位区域的热防护特点。研究发现，热流量及衣下空气层是影响热收缩形变的关键因素，手臂及腿部对应服装部位需重点防护，数据挖掘技术是探索服装舒适性与功能机制与特点的有效工具。

Data Mining of Protective Clothing Shrinkage During Flash Fire

With the application of the thermal manikin and fire manikin, mass data are produced during the experiment research in the clothing engineering area. The advantages of the larger samples can not be revealed using the conventional analysis method. Thus, in the paper, by using the Clementine software, data mining method is used to explore the data produced by flash fire experiment. The decision tree method and the neural net method are used to determine the key influence factors of the thermal shrinkage, which are then used in Kohonen cluster to divide protective clothing into different parts. Research shows that heat flux is the most important factor to the degree of shrinkage and the arm and leg are the key parts to be protected. It suggested that data mining is an effective tool to explore the character and function of the protective clothing.