M
Michael Steinbach
Researcher at University of Minnesota
Publications - 161
Citations - 15900
Michael Steinbach is an academic researcher from University of Minnesota. The author has contributed to research in topics: Cluster analysis & Knowledge extraction. The author has an hindex of 37, co-authored 156 publications receiving 12509 citations. Previous affiliations of Michael Steinbach include United States Geological Survey & IEEE Computer Society.
Papers
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Journal ArticleDOI
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.
OtherDOI
Introduction to Data Mining
TL;DR: This book discusses data mining through the lens of cluster analysis, which examines the relationships between data, clusters, and algorithms, and some of the techniques used to solve these problems.
Proceedings Article
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data.
TL;DR: A novel clustering technique that addresses problems with varying densities and high dimensionality, while the use of core points handles problems with shape and size, and a number of optimizations that allow the algorithm to handle large data sets are discussed.
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.