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Time Series Analysis and Forecasting: Chapter 5 Week 7 Data Mining: Chapter 6 Test 2 (Chapters 5 and 6) Week 8 Spreadsheet Models: Chapter 7 Week 9 Linear Optimization Models: Chapter 8 Week 10 Interger Linear Optimization Models: Chapter 9 Week 11 Network Flow Models Chapter 8 Week 12 Monte Carlo Simulation: Chapter Statistics Statistical Aspects of Data Mining Professor Rajan Patel Lecture 10 = Finish Chapter 5 Agenda: 1) Assign Homework #4 (due Aug 13th) 2) Lecture over more of Chapter 5.
2 11 Boosting (Section , page ) zBoosting works by upweighing points at each iteration. data mining techniques and how they can be applied to interact effectively with CTOs, expert data and submit homework on time, keep up with the content introduced in each session, and Chapter 15 Bring laptop 4/11 Recommender systems: Association rules, and PageRank WEKA lab session: Association rules.
data mining project to address a business problem, or a hands-on data project in which the team will address a business problem by applying data mining techniques to real business data. Deliverables: Each team will hand in a brief report (85%) and prepare a short presentation (15%) of their work.
Chapter 1 Exercises 1. What is data mining?In your answer, address the following: Data mining refers to the process or method that extracts or \mines" interesting knowledge or patterns from large amounts of data.
(a) Is it another hype? Data mining is not another hype. Instead, the need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for.
Time Series Analysis and Forecasting: Chapter 5 Week 7 Data Mining: Chapter 6 Test 2 (Chapters 5 and 6) Week 8 Spreadsheet Models: Chapter 7 Week 9 Linear Optimization Models: Chapter 8 Week 10 Interger Linear Optimization Models: Chapter 9 Week 11 Network Flow Models Chapter 8 Week 12 Monte Carlo Simulation: Chapter Data Mining is the modeling and analysis of data, usually very large datasets, for decision making. Although several software packages used for Data Mining will be reviewed and compared, the primary concepts will be illustrated using SAS. it focuses on data mining of very large amounts of data, that is, data so large it does not ﬁt in main memory. Because of the emphasis on size, many of our examples are about the Web or data derived from the Web. Further, the book takes an algorithmic point of view: data mining is about applying algorithms to data, rather than using data to.