The traditional k-prototypes algorithm is well versed in clustering data with mixed numeric and categorical attributes, while it is limited to complete data. In order to handle incomplete data set with missing values, an improved k-prototypes algorithm is proposed in this paper, which employs a new dissimilarity measure for incomplete data set with mixed numeric and categorical attributes and a...

We propose a fast, model agnostic method for finding interpretable counterfactual explanations of classifier predictions by using class prototypes. show that prototypes, obtained either an encoder or through specific k-d trees, significantly speed up the search instances and result in more explanations. quantitatively evaluate interpretability generated counterfactuals to illustrate effectivene...

The traditional design process has been developed, through time, by trial and error, following an evolutive approach. By this procedure, the team focused its attention on only one conceptual alternative at a which is perfected step until expected outcome obtained. Nevertheless nowadays, due to high complexity of ships increasingly stringent operational requirements, approach appears be obsolete...

This paper discusses the various challenges encountered during iterative testing of an insurance company’s home page prototypes. The study focuses on the methodology details, including the selection of prototypes and usability evaluation methods and the considerations for practical trade-offs. During Phase 1, six individual think-aloud interviews were conducted to compare three prototypes. Part...