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Data Mining and Visualization

Welcome to Data Mining and Visualization, Fall 2014 This course will be taught at Adams State University (ASU) in Alamosa, Colorado in conjunction with Embry-Riddle Aeronautical University (ERAU) in Daytona Beach, Florida. The course will be taught using a hybrid learning environment. Dr. Matthew Iklé at ASU will lead the course and broadcast live to…

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Welcome to Data Mining and Visualization, Fall 2014 This course will be taught at Adams State University (ASU) in Alamosa, Colorado in conjunction with Embry-Riddle Aeronautical University (ERAU) in Daytona Beach, Florida. The course will be taught using a hybrid learning environment. Dr. Matthew Iklé at ASU will lead the course and broadcast live to…
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Data Mining and Visualization | datamined.wordpress.com Reviews

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Welcome to Data Mining and Visualization, Fall 2014 This course will be taught at Adams State University (ASU) in Alamosa, Colorado in conjunction with Embry-Riddle Aeronautical University (ERAU) in Daytona Beach, Florida. The course will be taught using a hybrid learning environment. Dr. Matthew Iklé at ASU will lead the course and broadcast live to…

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1

Advanced Techniques « Data Mining and Visualization

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Data Mining and Visualization. Instructor: Dr. Matthew Iklé, Adams State University, Colorado. Relevant Topics and Helpful Links. How to Get Help. Summary: Putting everything together. 28 Oct 2014 video: SOMs, Genetic Algorithms. 31 Oct 2014 video: Data Mining Summary, Part 1. 3 Nov 2014 video: Data Mining Summary, Part 2. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:. Address never made public). Notify me of new comments via email.

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Homework List « Data Mining and Visualization

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Data Mining and Visualization. Instructor: Dr. Matthew Iklé, Adams State University, Colorado. Relevant Topics and Helpful Links. How to Get Help. Course Calendar and Test Dates. What is Data Mining? Scope and Goals of Course. Introduction to and Installation of Weka. Data Types and Basic Definitions. Data Preparation in Weka. Data Quality, Preprocessing, Similarity Measures. Exploring and Visualizing Data. Introduction to Classification: Decision Trees. Classification: Model Overfitting and Evaluation.

3

WEKA Documentation « Data Mining and Visualization

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Data Mining and Visualization. Instructor: Dr. Matthew Iklé, Adams State University, Colorado. Relevant Topics and Helpful Links. How to Get Help. Download Weka here: http:/ www.cs.waikato.ac.nz/ml/weka/downloading.html. Weka documentation is here: http:/ www.cs.waikato.ac.nz/ml/weka/documentation.html. ARFF format: http:/ weka.wikispaces.com/ARFF. XRFF format: http:/ weka.wikispaces.com/XRFF. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:.

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Course Modules « Data Mining and Visualization

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Data Mining and Visualization. Instructor: Dr. Matthew Iklé, Adams State University, Colorado. Relevant Topics and Helpful Links. How to Get Help. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:. Address never made public). You are commenting using your WordPress.com account. ( Log Out. You are commenting using your Twitter account. ( Log Out. You are commenting using your Facebook account. ( Log Out. Notify me of new comments via email.

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Matlab Tutorials « Data Mining and Visualization

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Data Mining and Visualization. Instructor: Dr. Matthew Iklé, Adams State University, Colorado. Relevant Topics and Helpful Links. How to Get Help. Https:/ www.mathworks.com/academia/student center/tutorials/register.html. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:. Address never made public). You are commenting using your WordPress.com account. ( Log Out. You are commenting using your Twitter account. ( Log Out. How to Get Help.

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Relevant Topics and Helpful Links « Data Mining and Visualization

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Data Mining and Visualization. Relevant Topics and Helpful Links. Data Preparation and Exploration. Advanced Topics in Data Mining. How to Get Help. Relevant Topics and Helpful Links. Dr Ikle’s Course Website in Data Mining and Visualization. Stanford, mathematics gives you wings. Dan Meyer, Math Curriculum to Makeover. MCM, Mathematical Contesting Modeling. COMAP, Consortium of Mathematics and its Applications. Matrix Algebra Tool Java Applet Simulations. MATLAB Tutorials for College Students.

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Announcements « Data Mining and Visualization

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Data Mining and Visualization. Relevant Topics and Helpful Links. Data Preparation and Exploration. Advanced Topics in Data Mining. How to Get Help. This course offered in Spring 2015 is mostly adapted from Dr. Ikle’s course materials. NSF also sponsored the development of other two courses in Computational Math and Data Science. All those courses are available at http:/ gps2dreamcollege.com/. Hong Liu, PhD. Click the Follow button on the right sidebar to get an email anytime announcements. You are comme...

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Data Mining and Visualization

Data Mining and Visualization. Instructor: Dr. Matthew Iklé, Adams State University, Colorado. Relevant Topics and Helpful Links. How to Get Help. Welcome to Data Mining and Visualization, Fall 2014. Menu The development of this course has been made possible by an NSF grant secured by Dr. Liu. The course will include a mixture of lectures, readings, homework, and an exam, culminating in a hands-on team project. The schedule for all student assignments will be updated on the course Google calendar&#46...

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Data Mining and Visualization

Data Mining and Visualization. Relevant Topics and Helpful Links. Data Preparation and Exploration. Advanced Topics in Data Mining. How to Get Help. Use the Calendar to access the attached slides of helping sessions. November 21, 2013. January 9, 2015. Welcome to Data Mining and Visualization, Spring 2015, EagleVision Connection to Classes. Under the Home menu. The development of this course has been made possible by an NSF grant. To find our current tasks. Your Instructor, Dr. Hong Liu. November 21, 2013.

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