  With the prevalence of e-commence websites and the ease of online shopping,
consumers are embracing huge amounts of various options in products.
Undeniably, shopping is one of the most essential activities in our society and
studying consumer's shopping behavior is important for the industry as well as
sociology and psychology. Indisputable, one of the most popular e-commerce
categories is clothing business. There arises the needs for analysis of popular
and attractive clothing features which could further boost many emerging
applications, such as clothing recommendation and advertising. In this work, we
design a novel system that consists of three major components: 1) exploring and
organizing a large-scale clothing dataset from a online shopping website, 2)
pruning and extracting images of best-selling products in clothing item data
and user transaction history, and 3) utilizing a machine learning based
approach to discovering fine-grained clothing attributes as the representative
and discriminative characteristics of popular clothing style elements. Through
the experiments over a large-scale online clothing shopping dataset, we
demonstrate the effectiveness of our proposed system, and obtain useful
insights on clothing consumption trends and profitable clothing features.
