Application of cluster analysis to fabric classification

This paper introduces a new way of classifying clothing fabrics objectively. Representative apparel fabrics were collected and measured by the Kawabata Evaluation System for Fabrics (KES‐FB). The disjoint clustering method was used to divide fabrics into four clusters, each representing particular fabric performance and end‐use characteristics. These classified clusters were further analyzed applying the method of principal‐component analysis to acquire factor patterns that indicate the most important fabric properties for characterizing different fabric end‐use. Extracted information from the instrumentally obtained data in terms of fabric physical properties is useful to fabric and garment producers, apparel designers, and consumers in specifying and categorizing fabric products, in insuring proper fabric use, and in controlling fabric purchase.