Interpretable Knowledge Discovery Reinforced by Visual Methods

Abstract

This tutorial covers the state-of-the-art research, development, and applications in the KDD area of interpretable knowledge discovery reinforced by visual methods to stimulate and facilitate future work. It serves the KDD mission and objectives of gaining insight from the data. The topic is interdisciplinary bridging of scientific research and applied communities in KDD, Visual Analytics, Information Visualization, and HCI. This is a novel and fast growing area with significant applications, and potential. First, in KDD, these studies have grown under the name of visual data mining. The recent growth under the names of deep visualization, and visual knowledge discovery, is motivated considerably by deep learning success in accuracy of prediction and its failure in explanation of the produced models without special interpretation efforts. In the areas of Visual Analytics, Information Visualization, and HCI, the increasing trend toward machine learning tasks, including deep learning, is also apparent. This tutorial reviews progress in these areas with a comparative analysis of what each area brings to the joint table. The comparison includes the approaches: (1) to visualize Machine Learning (ML) models produced by the analytical ML methods, (2) to discover ML models by visual means, (3) to explain deep and other ML models by visual means, (4) to discover visual ML models assisted by analytical ML algorithms, (5) to discover analytical ML models assisted by visual means. The presenter will use multiple relevant publications including his books: "Visual and Spatial Analysis: Advances in Visual Data Mining, Reasoning, and Problem Solving" (Springer, 2005), and "Visual Knowledge Discovery and Machine Learning" (Springer, 2018). The target audience of this tutorial consists of KDD researchers, graduate students, and practitioners with the basic knowledge of machine learning.