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25th ACM SIGKDD
Conference on Knowledge Discovery and Data Mining.
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Tutorial 21 Time: 13:00 PM -17:00 PM on August 4, 2:30-3pm Break,
Place: William A. Egan Civic & Convention Center 555 W 5th Ave, Anchorage, AK 99501 Summit 11-Ground Level Interpretable Knowledge Discovery
Reinforced by Visual Methods Dept. of Computer Science, Central Washington University,
USA This tutorial will cover 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 will serve the KDD mission of gaining insight
from data. The topic is interdisciplinary bridging 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 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 evident. This tutorial will review progress
in these areas with a comparative analysis of what each area brings to the
joint table. The comparison will include 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 an analytical ML model assisted
by visual means. The presenter will use multiple relevant publications
including his books: "Visual Knowledge Discovery and Machine
Learning" and "Visual and Spatial Analysis: Advances in Visual Data
Mining, Reasoning, and Problem Solving" published in Springer. The
target audience of this tutorial
consists of KDD researches, graduate students, and practitioners with the
basic knowledge of KDD, data mining, and machine learning. The necessary
information on the visualization and visual analytics methods will be
provided. |
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Dr.
Boris Kovalerchuk is
a professor of Computer Science at Central Washington University, USA. His
publications include three books "Data Mining in Finance " (Springer, 2000), "Visual and Spatial
Analysis" (Springer, 2005), and "Visual Knowledge Discovery and
Machine Learning" (Springer, 2018), a chapter in the Data Mining
Handbook and over 170 other publications. His research interests are in data
mining, machine learning, visual analytics, uncertainty modeling, data
fusion, relationships between probability theory and fuzzy logic, image and
signal processing. Dr. Kovalerchuk has been a principal investigator of
research projects in these areas supported by the US Government agencies. He
served as a senior visiting scientist at the US Air Force Research Laboratory
and as a member of expert panels at the international conferences and panels
organized by the US Government bodies. |
