P
Pang-Ning Tan
Researcher at Michigan State University
Publications - 200
Citations - 13333
Pang-Ning Tan is an academic researcher from Michigan State University. The author has contributed to research in topics: Cluster analysis & Association rule learning. The author has an hindex of 43, co-authored 191 publications receiving 11436 citations. Previous affiliations of Pang-Ning Tan include University of Minnesota & United States Department of the Army.
Papers
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Book ChapterDOI
An incremental data stream clustering algorithm based on dense units detection
TL;DR: In this article, an incremental clustering method based on dense units detection is proposed for clustering data streams, which is motivated by the needs to develop a single-pass algorithm that is capable of detecting evolving clusters, and yet requires little memory and computation time.
Journal ArticleDOI
Climate Scenario Development and Applications for Local⁄Regional Climate Change Impact Assessments: An Overview for the Non-Climate Scientist Part I: Scenario Development Using Downscaling Methods
Julie A. Winkler,G. Guentchev,Perdinan,Pang-Ning Tan,Sharon Zhong,Malgorzata Liszewska,Zubin Abraham,Tadeusz Niedźwiedź,Zbigniew Ustrnul +8 more
TL;DR: In this article, a review of approaches to climate downscaling is presented, focusing on three broad categories: dynamic, empirical-dynamic and disaggregation methods, and the fundamental considerations of different methods are highlighted and explained for non-climatologists.
Proceedings ArticleDOI
Semi-supervised outlier detection
TL;DR: This paper is concerned with employing supervision of limited amount of label information to detect outliers more accurately, with an objective function that punishes poor clustering results and deviation from known labels as well as restricts the number of outliers.
Proceedings ArticleDOI
Generalizing the notion of support
TL;DR: A framework for generalizing support is described that is based on the simple, but useful observation that support can be viewed as the composition of two functions: a function that evaluates the strength or presence of a pattern in each object (transaction) and afunction that summarizes these evaluations with a single number.
Detection of Novel Network Attacks Using Data Mining
Levent Ertoz,Eric Eilertson,Aleksandar Lazarevic,Pang-Ning Tan,Paul Dokas,Vipin Kumar,Jaideep Srivastava +6 more
TL;DR: Experimental results on live network traffic at the University of Minnesota show that the MINDS anomaly detection techniques have been successful in automatically detecting several novel intrusions that could not be identified using state-of-the-art signature-based tools such as SNORT.