V
Vipin Kumar
Researcher at University of Minnesota
Publications - 678
Citations - 67181
Vipin Kumar is an academic researcher from University of Minnesota. The author has contributed to research in topics: Parallel algorithm & Computer science. The author has an hindex of 95, co-authored 614 publications receiving 59034 citations. Previous affiliations of Vipin Kumar include University of Maryland, College Park & United States Department of the Army.
Papers
More filters
Journal ArticleDOI
Anomaly Detection for Discrete Sequences: A Survey
TL;DR: A comprehensive and structured overview of the existing research for the problem of detecting anomalies in discrete/symbolic sequences is provided in this article, where the authors provide a global understanding of the sequence anomaly detection problem and how existing techniques relate to each other.
Proceedings ArticleDOI
Feature bagging for outlier detection
Aleksandar Lazarevic,Vipin Kumar +1 more
TL;DR: A novel feature bagging approach for detecting outliers in very large, high dimensional and noisy databases is proposed, which combines results from multiple outlier detection algorithms that are applied using different set of features.
Journal ArticleDOI
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
Anuj Karpatne,Gowtham Atluri,James H. Faghmous,Michael Steinbach,Arindam Banerjee,Auroop R. Ganguly,Shashi Shekhar,Nagiza F. Samatova,Vipin Kumar +8 more
TL;DR: The paradigm of theory-guided data science is formally conceptualized and a taxonomy of research themes in TGDS is presented and several approaches for integrating domain knowledge in different research themes are described using illustrative examples from different disciplines.
BookDOI
The Top Ten Algorithms in Data Mining
Xindong Wu,Vipin Kumar +1 more
TL;DR: Identifying some of the most influential algorithms that are widely used in the data mining community, this volume provides a description of each algorithm, discusses the impact of the algorithms, and reviews current and future research on the algorithms.
Journal ArticleDOI
Selecting the right objective measure for association analysis
TL;DR: This paper describes several key properties one should examine in order to select the right measure for a given application and presents an algorithm for selecting a small set of patterns so that domain experts can find a measure that best fits their requirements by ranking this smallSet of patterns.