Network Data Mining And Analysis

From Social Data Mining and Analysis to Prediction and Community Detection (Repost)

Kaya, Mehmet Kaya, Özcan Erdoǧan, "From Social Data Mining and Analysis to Prediction and Community Detection"
English | 2017 | pages: 248 | ISBN: 3319513664 | PDF | 6,2 mb

Network Data Mining And Analysis  eBooks & eLearning

Posted by readerXXI at Feb. 17, 2019
Network Data Mining And Analysis

Network Data Mining And Analysis
by Ming Gao, Ee-Peng Lim
English | 2018 | ISBN: 9813274956 | 205 Pages | PDF | 12.5 MB

Network Role Mining and Analysis  eBooks & eLearning

Posted by step778 at Dec. 17, 2021
Network Role Mining and Analysis

Derek Doran, "Network Role Mining and Analysis"
English | 2017 | pages: 109 | ISBN: 3319538853 | PDF | 2,1 mb

More Data Mining with R  eBooks & eLearning

Posted by IrGens at Aug. 12, 2020
More Data Mining with R

More Data Mining with R
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 10h 34m | 6.06 GB
Instructor: Geoffrey Hubona, Ph.D.

Data Mining and Business Analytics with R  eBooks & eLearning

Posted by AvaxGenius at April 13, 2020
Data Mining and Business Analytics with R

Data Mining and Business Analytics with R by Johannes Ledolter
English | PDF(True) | 2013 | 360 Pages | ISBN : 111844714X | 41.57 MB

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Data Mining and the Analytics Workflow  eBooks & eLearning

Posted by IrGens at Nov. 8, 2019
Data Mining and the Analytics Workflow

Data Mining and the Analytics Workflow
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 55m | 400 MB
Instructor: Janani Ravi

Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods  eBooks & eLearning

Posted by AvaxGenius at Nov. 17, 2020
Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods

Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods by Steven Finlay
English | EPUB | 2014 | 261 Pages | ISBN : 1137379278 | 2.1 MB

This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.

Network Role Mining and Analysis (Briefs in Complexity)  eBooks & eLearning

Posted by Free butterfly at Nov. 10, 2019
Network Role Mining and Analysis (Briefs in Complexity)

Network Role Mining and Analysis (Briefs in Complexity) by Derek Doran
English | March 21, 2017 | ISBN: 3319538853 | 116 pages | PDF | 2.08 Mb

Data Mining: The Textbook  eBooks & eLearning

Posted by AvaxGenius at Feb. 22, 2022
Data Mining: The Textbook

Data Mining: The Textbook by Charu C. Aggarwal
English | PDF(True) | 2015 | 746 Pages | ISBN : 3319141414 | 16.4 MB

This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way.

Advances in K-means Clustering: A Data Mining Thinking  eBooks & eLearning

Posted by AvaxGenius at Jan. 25, 2024
Advances in K-means Clustering: A Data Mining Thinking

Advances in K-means Clustering: A Data Mining Thinking by Junjie Wu
English | PDF (True) | 2012 | 187 Pages | ISBN : 3642298060 | 4.4 MB

Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.