Introduction to data mining pang ning tan

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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.5/5(1). Introduction to Data Mining has 268 ratings and 14 reviews. Xinyu said: As an introductory book, this book does a really good job. Explain well and easy 4/5(14). Introduction to Data Mining [Pang-Ning Tan, Michael Steinbach, Vipin Kumar] on Amazon.com. *FREE* shipping on qualifying offers. International reprint edition.3.7/5(56).
Introduction to Data Mining Pang-Ning Tan, Provides both theoretical and practical coverage of all data mining topics. Pang-Ning Tan, Michigan State Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining 1 Introduction. 1.1 What. Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.5/5(1). Introduction to Data Mining has 268 ratings and 14 reviews. Xinyu said: As an introductory book, this book does a really good job. Explain well and easy 4/5(14). Introduction to Data Mining [Pang-Ning Tan, Michael Steinbach, Vipin Kumar] on Amazon.com. *FREE* shipping on qualifying offers. International reprint edition.3.7/5(56).
Introduction to Data Mining
by Pang-Ning Tan and Michael Steinbach and Vipin Kumar


Overview - Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.  Read more.


 

 

 

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Introduction to Data Mining by Pang-Ning Tan

Introduction to Data Mining [Pang-Ning Tan, Michael Steinbach, Vipin Kumar] on Amazon.com. *FREE* shipping on qualifying offers. International reprint edition.3.7/5(56). 2. Suppose that you are employed as a data mining consultant for an In-ternet search engine company. Describe how data mining can help the company by giving specific examples of how techniques, such as clus-tering, classification, association rule mining, and anomaly detection can be applied. The following are examples of possible answers. Introduction to Data Mining, (First Edition) Authors: Feilong Chen, Jerry Scripps, Pang-Ning Tan, Link Mining for a Social Bookmarking Web Site. Introduction to Data Mining Dr. Sanjay Ranka Professor Computer and Information Science and Engineering – Introduction to Data Mining by Pang-Ning Tan. Sep 06, 2004 · Scribd is the world's largest social reading and [eBook - EnG] Introduction to Data Mining (P. N. Tan, M. Steinbach, V. Kumar PANG-NING TAN 1/5(18). Introduction to Data Mining (2nd Edition) Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar Addison Wesley, ISBN-13: 978-0133128901 Instructor Resources.

Introduction to Data Mining [Pang-Ning Tan, Michael Steinbach, Vipin Kumar] on Amazon.com. *FREE* shipping on qualifying offers. International reprint edition.3.7/5(56). Introduction to Data Mining has 268 ratings and 14 reviews. Xinyu said: As an introductory book, this book does a really good job. Explain well and easy 4/5(14). Jan 01, 2005 · Introduction to Data Mining has 268 ratings and 14 reviews. Xinyu said: As an introductory book, this book does a really good job. Explain well and easy 4/5(14).


General Information:

Dr Pang-Ning Tan is a Professor in the Department of Computer Science and Engineering at Michigan State University. He received his M.S. degree in Physics and Ph.D. degree in Computer Science from University of Minnesota. His research interests focus on the development of novel data mining algorithms for a broad range of applications, including climate and ecological sciences, cybersecurity, and network analysis. He has published more than 130 technical papers in the area of data mining, including top conferences and journals such as KDD, ICDM, SDM, CIKM, and TKDE. He also served as associate editor and program committee chairs for several international journals and conferences. His research has been supported by the National Science Foundation, Office of Naval Research, Army Research Office, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, National Institutes of Health, and Michigan State University.

Click here for his latest CV, including publications.

Recent Publications:

  • Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan, Xi Liu and Lifeng Luo. WISDOM: Weighted Incremental Spatio-Temporal Multi-Task Learning via Tensor Decomposition. In Proceedings of IEEE International Conference on Big Data, Washington, DC (2016).

  • Jianpeng Xu, Pang-Ning Tan, Lifeng Luo, and Jiayu Zhou. GSpartan: a GeoSpatio-Temporal Multi-task Learning Framework for Multi-location prediction. In Proceedings of SIAM International Conference on Data Mining (SDM-2016), Miami, FL (2016).

  • Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan, and Kaixiang Lin. Synergies that Matter: Efficient Interaction Selection via Sparse Factorization Machine. In Proceedings of SIAM International Conference on Data Mining (SDM-2016), Miami, FL (2016).

  • Ding Wang, Prakash Mandayam Comar, and Pang-Ning Tan. Crowdsourcing of Network Data In Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN- 2016), Vancouver, Canada (2016).

  • Courtland VanDam and Pang-Ning Tan. Hashtag Hijacking from Twitter Data. In Proceedings of ACM Web Science Conference (WebSci-2016), Hanover, Germany (2016).

  • Xi Liu, Han Hee Song, Mario Baldi, and Pang-Ning Tan. Macro-scale Mobile App Market Analysis using Customized Hierarchical Categorization. In Proceedings of IEEE International Conference on Computer Communications (INFOCOM-2016), San Francisco, CA (2016).

  • Shuai Yuan, Pang-Ning Tan, Kendra Cheruvelil, Sarah Collins, and Patricia Soranno. Constrained Spectral Clustering for Regionalization: Exploring the Trade-off between Spatial Contiguity and Landscape Homogeneity. In Proceedings of the 2015 IEEE Int'l Conf on Data Science and Advanced Analytics, Special Session on Environmental and Geo-spatial Data Analytics, Paris, France (2015).

  • Lei Liu, Pang-Ning Tan, and Xi Liu. MF-Tree: Matrix Factorization Tree for Large Multi-Class Learning. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM-2015), Melbourne, Australia (2015).

  • Jianpeng Xu, Jiayu Zhou, and Pang-Ning Tan. FORMULA: Factorized Multi-task Learning for task discovery in personalized medical models. In Proceedings of SIAM International Conference on Data Mining (SDM-2105), Vancouver, Canada (2015).

  • Patricia A Soranno, Edward G Bissell, Kendra S Cheruvelil, Samuel T Christel, Sarah M Collins, C Emi Fergus, Christopher T Filstrup, Jean-Francois Lapierre, Noah R Lottig, Samantha K Oliver, Caren E Scott, Nicole J Smith, Scott Stopyak, Shuai Yuan, Mary Tate Bremigan, John A Downing, Corinna Gries, Emily N Henry, Nick K Skaff, Emily H Stanley, Craig A Stow, Pang-Ning Tan, Tyler Wagner, and Katherine E Webster. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse.. GigaScience, 4(1): 1-15 (2015).

  • Jianpeng Xu, Pang-Ning Tan, and Lifeng Luo. ORION: Online Regularized multI-task regressiON and its application to ensemble forecasting. In Proceedings of the IEEE International Conference on Data Mining (ICDM-2014), Shenzhen, China, December 14-17 (2014).

  • Zubin Abraham, Pang-Ning Tan, Perdinan, Julie Winkler, Shiyuan Zhong, Malgorzata Liszewska. Contour Regression: A Distribution Regularized Regression Framework for Climate Modeling. Statistical Analysis and Data Mining, 7(4): 272-281 (2014).

  • Lei Liu, Sabyasachi Saha, Ruben Torres, Jianpeng Xu, Pang-Ning Tan, Antonio Nucci, and Marco Mellia. Detecting Malicious Clients in ISP Network Using HTTP Connectivity Graph and Flow Information. In Proceedings of the IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM 2014), Beijing, China, August 17-20 (2014).

  • Zubin Abraham, Pang-Ning Tan, Perdinan, Julie Winkler, Shiyuan Zhong, Malgorzata Liszewska. Position-Preserving Multi-Output Prediction In Proceedings of the European Conference on Machine Learning (ECML-2013), Prague, Czech Republic, September 23-27 (2013).

  • Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, Pang-Ning Tan, and Antonio Nucci. Missing or Inapplicable: Treatment of incomplete continuous-valued features in supervised learning. In Proceedings of the SIAM International Conference on Data Mining (SDM-2013), Austin, Texas (2013).

  • Zubin Abraham, Pang-Ning Tan, Perdinan, Julie Winkler, Shiyuan Zhong, Malgorzata Liszewska. Distribution Regularized Regression Framework for Climate Modeling. In Proceedings of the SIAM International Conference on Data Mining (SDM-2013), Austin, Texas (2013).

  • Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, Pang-Ning Tan, and Antonio Nucci. Combining Supervised and Unsupervised Learning for Zero-Day Malware Detection. In Proceedings of 32nd IEEE International Conference on Computer Communications (INFOCOM- 2013), Turin, Italy, April 14-19 (2013).

  • Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, Pang-Ning Tan, and Antonio Nuc Weighted Linear Kernel with Tree Transformed Features For Malware Detection, In Proceedings of the 21st ACM International Conference on Information and Knowledge (CIKM 2012), Maui, Hawaii, October 29 - November 2 (2012).

  • Lei Liu, Prakash Mandayam Comar, Sabyasachi Saha, Pang-Ning Tan, and Antonio Nucc Recursive NMF: Efficient Label Tree Learning for Large Multi-Class Problems, In Proceedings of the 21st International Conference on Pattern Recognition (ICPR-2012), Tsukuba, Japan, November 11 - 16 (2012).

  • Prakash Mandayam-Comar, Pang-Ning Tan, and Anil K. Jain. A Framework for Joint Community Detection Across Multiple Related Networks, Neurocomputing, 76(1), pp 93-104 (2012).

  • Prakash Mandayam-Comar, Pang-Ning Tan, and Anil K. Jain. Simultaneous Classification and Community Detection on Heterogeneous Network Data, Data Mining and Knowledge Discovery, 25(3), pp. 420-449 (2012)


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