Data mining is a major areas of exploration for knowledge discovery in databases. This topic has gained great relevance especially in the 1990’s and early 2000’s with web data growing at an exponential rate. As more data are collected by businesses and scientific institutions alike,knowledge exploration techniques are needed to gain useful business intelligence. This course will cover a wide spectrum of industry standard techniques using widely available database and tools packages for knowledge discovery.

Data mining is for relatively unstructured data for which more sophisticated techniques are needed. The course aims to cover powerful data mining techniques including clustering, association rules, and classification. We also introduce  high volume data processing mechanisms by building warehouse schemas such as snowflake  and star. OLAP query retrieval techniques are also introduced.  We learn the basics of Data Warehousing structure and query formulation, as it impacts a data miner.  We do not query against  an actual  data warehouse.  There are other courses for that, if you are interested in Data Warehousing.

COMP 251: Introduction to Database Systems or COMP 271: Data Structures  

Title: Data Mining: Concepts and Techniques, Third Edition
Authors: Jiawei Han, Micheline Kamber, Jian Pei
Publisher: Morgan Kaufmann; 3^rd edition (July 6, 2011)
ISBN-10: 0123814790  or try this

Title:  Data Mining for the Masses, 2ed, with Implementations in RapidMiner and R
Author:  Matthew North
ISBN-13: 978-1523321438

You can buy the hard copy on Amazon, or free older version here.  There is also a support site for the second edition, including all datasets as well as very comprehensive PPT slides.  This may be sufficient for the additional R topics that are not covered in the first (free) edition.

Nice reference book, if you want to read more about data warehousing:
Title:  The Data Warehouse Toolkit, third edition
Authors:  Ralph Kimball and Margy Ross
Publisher: Wiley
ISBN:  978-1-118-53080-1

Course Objectives and Goals

After taking this course, students should be able to:

• Know basic techniques for both supervised and unsupervised knowledge discovery.
• Know and use software package techniques for mining.
• Apply R to data preparation and basic DM algorithms
  
• Have a good grasp of data mining techniques: classification, association rules, clustering, etc.
• Understand the impact of  data warehousing schemas on  the selection of Data Mining algorithms 

• This is course is not a programming course in the data mining algorithms.
• Neither is it a strictly applications course, where you don't have to know the algorithms.
• What this course is:  This course is a (hopefully) happy medium, where you learn what the algorithms are and how they work, so that you can then use them appropriately and interpret the results.  The course keeps evolving towards a more application orientation.

• Attendance:  Attendance refers to physical attendance for the on-campus class and logging into Sakai for the online class.  Most of the attendance policies do NOT apply to the online class, although please be aware the Sakai does keep track of the frequency and duration of visits.  For the on-campus class, attendance may be taken at any time, hopefully every session.  While there is no graded component for attendance, any assignments and labs that are completed in class may not be made up for any reason.  If for any reason you do miss a class session, it is your responsibility to determine what you missed, locate any handouts, determine any changes in assignments, course plans or schedules, etc. If you have to miss many classes, even for a very valid reason, you may want to consider taking this course another semester.  You cannot get credit for work that you were unable to complete in class, even if your reason for missing class is valid.  The exception is for religious holidays, which I am required to accommodate.  
• Late homeworks and labs:  If you have a very good reason for submitting something late, you may ask me, and I may allow you to submit it late for half credit. I will only allow this once or maybe twice during the semester.  Habitual lateness will earn you zeros.
• Labs:  During class, we will be working on specific lab assignments, which must be completed in class unless otherwise indicated.  These will be hands-on applications of the concepts and algorithms covered in class.  Often, labs will simply be checked in class to see that you have completed them.  You may be asked to submit them on Sakai.  Sometimes, there may be a small lab assignment to exercise the techniques that we cover.   Data Mining is an iterative process, and we will be exploring the data together.  On campus students must be present for labs to get credit for them, unless I specify that you may work off the online videos.    You should complete the installation of RapidMiner on your own as soon as possible, preferably before class begins.  RapidMiner is not installed in LUC labs.  You will have to install the software on your laptops.  If you do not have a laptop, team up with someone who does.  If that doesn't work, I can set up a VM for you, but I need to know before class begins.  Online students do not have to complete labs in a physical classroom, but must complete them by the dates shown for submission, which correspond to the in-person class sessions.
• Homework Assignments:  I have cut back on the homework assginments, in favor of additional labs.  For your convenience, all homework assignments (except for the Orientation ones) are in a single Excel spreadsheet.  Please note that the homework problems have been reconstructed so that the values are not the same as in the text; nor are they the same as the last time I taught the course.  This means that you really have to do the homework.  For the concept questions that do not require calculations, please be very careful to state the answers in your own words.  Do not  copy from the textbook.  Do not copy from the Instructor's Manual.  Do not  copy from other students, either in this class or previous classes.  If you do, you will be subject to the penalites for Academic Honesty violations. There are many different models and algorithms covered in the course.  The purpose of the homeworks is to give you prompt application of the important concepts and algorithms, as well as prompt feedback on your understanding of the material.  Practically speaking, the homeworks will prepare you for the exam, which is not lab-based.
• Each tab in the homework spreadsheet is a separate problem.   You may add worksheets and submit your homework in the same spreadsheet (which may be helpful to you in preparing for the exam), or you may submit a separate document.  Or, you may keep your spreadseet in Google Drive, Box.com or Github and submit a link on Sakai.    Handwritten work should be limited to those graphs or charts that may be difficult or cumbersome to create with software.  You must always submit something on Sakai:  either the homework itself, or a comment that it was submitted in person (when and to whom), or a comment that a team member submitted the homework on your behalf.  If you are submitting on behalf of your team, please indicate in your comment the names of the students in the team.   If you do not, you have no valid complaint against me for not giving you credit for the homework if your team member submitted it on your behalf.  It is not sufficient for your team member to submit your name.  You must also submit a comment and submit it as your assignment.
• Exams:  There are two exams.  Past experience has shown that students prefer to have the midterm before Spring Break rather than after.  Therefore, the Midterm is scheduled for Week 7 (before Spring Break), and the Final Exam is during finals week, as scheduled by the university.   In the case of a medical emergency, I will determine if a makeup exam will be administered, or if another accommodation will be made.  Medical emergencies must be documented and will be verified.  Please note that I am very strict about not allowing makeup exams except in the most extreme emergencies. You are aware of the Academic Calendar before the semester begins.  Do not schedule travel plans or vacations on exams dates.  The online class will be informed as to the time window and the exact format of the online exam. If at all possible, I will try to schedule in-person proctoring dates and times for online students.   There is a small chance that I may eliminate the final exam in favor of more project time.  This depends on what I see in the projects "walk-arounds" and team meetings, and it is totally at my discretion.  Students will be informed of any change in the project peresnetation schedule or in the final exam no later than April 15.
• Project:  You are required to design and implement a project that analyzes a large dataset, using some of the techniques and algorithms that are covered in the course.  The project presentations will be on 4/25.  If more time is needed, presentations will be scheduled on 4/18 or 5/02.  I will let you know well in advance if you have to be ready by 4/18.   Projects may be completed in teams; however, every team member is expected to fully participate in the project in order to earn the team grade.  There are more details about the project in the project assignments.
• Project Excellence:   A "very good" project that fulfills the requirements and is perfectly satisfactory can earn full points for the Project.  That would be 150 points.  If you get full points on your project and do not earn any Project Excellence points, please don't ask me what you did "wrong".  The answer will most likley be  "nothing".  Project Excellence points can be earned for a project that is truly excellent, incorporating components well beyond the material covered in class, or simply having a real "Wow" factor.  A grade of "A" means "excellent".  There are many points in this course that could be considered free points.  Full points for Homework and Labs can be earned if significant effort is demonstrated:  you are graded for completeness, not necessasrily for correctlness (although grossly incorrect answers will be makred off).  But for a project to get an A, it must be excellent.  As I review your progress during project walkthroughs, I will tell you what you can do to earn excellence points for your project.
• Extra Credit:  There is no extra credit in this course, as this is not practical to the course, nor is it fair to other students.
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Course Schedule

This schedule is a guide.  Exact dates and topics may be subject to change.  It is my best estimate, but we may have to adjust the schedule slightly.  You are responsible for all announcement/changes made in class or posted on Sakai.