Data Mining Data Warehousing Lab Manual Data mining techniques: for marketing, sales, and customer, the leading introductory book on data mining, fully updated and revised! When berry and linoff.
Data mining, data mining course, graduate data mining, financial data mining, machine learning, neural networks, genetic programs, decision trees, WEKA CSCI 5833 -- Data Mining Tools and Techniques STAT 5931 -- Research Topics in Statistics Updated August 29, 2018 Office and Addresses Delta 171 Phone 281.283.3805 email: Secretary: Ms. Caroline Johnson, Delta 161 281.283.3860 Face-to-Face Class Hours Wednesday 4:00 - 7:50, Delta 242 Office Hours Wed. 1 - 4, or by appointment. If the suite door is locked, then call my extension (last 4 digits) using the phone in the hallway. Teaching Assistant Ms. Rekha Sampangiramaiah Email: Hours: Monday 3 - 5 and 7 to 10; Tuesday 1 - 4 and 7 to 10; Wednesday 1 - 4 Course Description Data Mining has emerged as one of the most exciting and dynamic fields in computer science.
The driving force for data mining is the presence of petabyte-scale online archives that potentially contain valuable bits of information hidden in them. Windows 7 Pro Oa Sea Hp Drivers. Commercial enterprises have been quick to recognize the value of this concept; consequently, within the span of a few years, the software market itself for data mining is expected to be in excess of $10 billion by the end of this year. The theoretical underpinnings of the data mining have existed for awhile (e.g., pattern recognition, statistics, data analysis and machine learning), the practice and use of these techniques have been largely ad-hoc.
With the availability of large databases to store, manage and assimilate data, the new thrust of data mining lies at the intersection of database systems, artificial intelligence and algorithms that efficiently analyze data. Data mining seeks to detect `interesting' and significant nuggets of relationships/knowledge buried within data. It seeks to discover association rules, episode rules, sequential rules, etc., and it is concerned with efficient data structures and algorithms for data examination which possess good scaling properties. There have been several success stories in this relatively young area: the SKICAT system for automatic cataloguing of sky surveys (JPL), the Advanced Scout system for mining NBA data (IBM), the QuakeFinder system for geoscientific data mining (UCLA/JPL) and the PYTHIA system for mining information from performance evaluation of scientific software (Purdue). Case studies from various domains (financial, bioinformatics, etc.) will be presented.
The traditional graduate student load is 3 courses. Be prepared to commit 15 to 20 hours per week to this course! Course Goals By the end of the course, you will • Understand the data mining process.
• Have a working knowledge of different data mining tools and techniques. Nec 660 Printer Driver Support on this page. • Have an understanding of various Machine Learners (ML).
• Have a working knowledge of some of the more significant current research in the area of data mining and ML. • Be aware of various data mining data repositories for the study of data mining. • Be able to effectively apply a number of data mining algorithms (e.g., neural networks, genetic algorithms) to solve data mining problems from various problem domains including Financial and Bioinformatics. • Be familiar with several successful applications of data mining. Prerequisites A course in artificial intelligence, machine learning, pattern recognition, algorithms, or statistics would be helpful, but is not required. Programming experience (or at least one course) in either C, C++, C#, Delphi, Java, PASCAL, or VB (using Visual Studio). If you do not meet the prerequisites, then you need to drop this course!