Intelligible Machine Learning and Knowledge Discovery Boosted by Visual Means

Document Type

Conference Proceeding

Department or Administrative Unit

Computer Science

Publication Date



Intelligible machine learning and knowledge discovery are important for modeling individual and social behavior, user activity, link prediction, community detection, crowd-generated data, and others. The role of the interpretable method in web search and mining activities is also very significant to enhance clustering, classification, data summarization, knowledge acquisition, opinion and sentiment mining, web traffic analysis, and web recommender systems. Deep learning success in accuracy of prediction and its failure in explanation of the produced models without special interpretation efforts motivated the surge of efforts to make Machine Learning (ML) models more intelligible and understandable. The prominence of visual methods in getting appealing explanations of ML models motivated the growth of deep visualization, and visual knowledge discovery. This tutorial covers the state-of-the-art research, development, and applications in the area of Intelligible Knowledge Discovery, and Machine Learning boosted by Visual Means.


This article was originally published in WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining. The full-text article from the publisher can be found here.

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WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining


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