LibGuides: SoTE: Data Mining: TIM-8130 Supplemental Resources

These are supplemental resources for TIM-8130. While intended primarily for students in that course to use as a supplement to their course materials, they may also be useful for others interested in data mining

Week One

Feedback loop
Carver, C. (2007). Feedback loop. In R. F. Baumeister & K. D. Vohs (Eds.), Encyclopedia of social psychology, (Vol. 1, pp. 350-351). ThousandOaks, CA: SAGE Publications, Inc. doi: 10.4135/9781412956253.n217

Review information on Feedback loop.
Data mining: Practical machine learning tools and techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.) [PDF File].

Read Chapter 1: “What’s It All About?”
Review information on data mining techniques.
The age of big data
BBC (Producer). (2013). The age of big data [Video file].

Watch the video about the use of big data.

Week Two

Data mining: Practical machine learning tools and techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.) [PDF File].

Read Chapter 7: “Data Transformations”
Review information on how big data is mined.

Week Three

Business statistics
Duncan, S. (Practitioner). (2016). Business statistics [Video file].

Review information on the basics of statistics.
Data mining: Practical machine learning tools and techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.) [PDF File].

Read Chapter 4: “Algorithms: The Basic Methods”
Review information on statistics in data mining.

Week Four

Automation of generalized additive neural networks for predictive data mining
de Waal, D. A., & du Toit, J. V. (2011). Automation of generalized additive neural networks for predictive data mining. Applied Artificial Intelligence, 25(5), 380-425.

Review the article for information on predictive data mining.
What is missing? Using data mining techniques with business cycle phases for predicting company financial crises
I-Hsien, T., & Yu-Cheng, L. (2011). What is missing? Using data mining techniques with business cycle phases for predicting company financial crises. Asia Pacific Management Review, 16(4), 535-549.

Review the article on how data mining is used as a predictor.
Origin of Markov chains
Kahn Academy Labs. (2014, April 28). Origin of Markov chains [Video file].

Watch the video to learn about how Markov chains were created.

Week Five

Data mining and rough set theory
Grzymala-Busse, J. W., & Ziarko, W. (2000). Data mining and rough set theory. Communications Of The ACM, 43(4), 108-109.
Review the article for information on the data mining theories.
Principles and theory for data mining and machine learning
Kleine, L. L. (2010). Principles and theory for data mining and machine learning. Journal Of The Royal Statistical Society: Series A (Statistics In Society), 173(3), 691-692.

Review the article for information on the theories of data mining and machine learning.
Data mining: Practical machine learning tools and techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.) [PDF File].

Read Chapter 5: “Credibility: Evaluating What’s Been Learned”
Review the information on machine learning in today’s organizations.

Week Six

Data mining: Practical machine learning tools and techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.) [PDF File].

Read Chapter 6: “Implementations: Real Machine Learning Schemes”
Review the information on data analytics within data mining.

Week Seven

AMA-TV: Big data, rejection, customer forecasting, and social media security
Mauricio, R., & Kovacs, E. (Practitioners). (2013). AMA-TV: Big data, rejection, customer forecasting, and social media security [Video file].

Review the video of information for data security within social media data sets.

Week Eight

The future of data mining – Predictive analysis
Agosta, L. (2004). The future of data mining – Predictive analysis. DM Review, 14(8), 16-37.
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Review the information on the future of data mining.
Using big data to create innovative new products
Butler, J. (Practitioner). (2016). Using big data to create innovative new products [Video file].

Review the video on how big data is used for innovation.