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
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Anomaly detection: A survey
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
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A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
George Karypis,Vipin Kumar +1 more
TL;DR: This work presents a new coarsening heuristic (called heavy-edge heuristic) for which the size of the partition of the coarse graph is within a small factor of theSize of the final partition obtained after multilevel refinement, and presents a much faster variation of the Kernighan--Lin (KL) algorithm for refining during uncoarsening.
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Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
A Comparison of Document Clustering Techniques
TL;DR: This paper compares the two main approaches to document clustering, agglomerative hierarchical clustering and K-means, and indicates that the bisecting K-MEans technique is better than the standard K-Means approach and as good or better as the hierarchical approaches that were tested for a variety of cluster evaluation metrics.
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Chameleon: hierarchical clustering using dynamic modeling
TL;DR: Chameleon's key feature is that it accounts for both interconnectivity and closeness in identifying the most similar pair of clusters, which is important for dealing with highly variable clusters.