last updated: December 15, 3p

COSC 6397--- Data Mining Fall 2005 (Dr. Eick )



Teaching Assistant: Rachsuda Jiamthapthaksin
www: Rachsuda's Website
e-mail: rachsuda@cs.uh.edu
office hours: TU 10-11a TH 11a-noon

Goals of the Data Mining Course

Data mining centers on finding novel, interesting, and potentially useful patterns in data. It aims at transforming a large amount of data into a well of knowledge. Data mining has become a very important field in industry as well as academia. For example, 630 papers were submitted for the 2005 IEEE International Conference on Data Mining (ICDM). Data mining tools and suites (for example, see KDnuggets' DM Software Survey) are used a lot in industry and in reseach projects. UH's Data Mining and Machine Learning Group (UH-DMML) conducts research in some of the areas that are covered by this course.

The course discusses the most important data mining techniques and provides background knowledge on how to approach a data mining task. Moreover, the application of data mining techniques will be studied in homework assignements and a course project.

Comments concerning this website

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Basic Course Information

Instructor: Dr. Christoph F. Eick
office hours (589 PGH): TU 2:30-3:30p TH 11:30a-12:30p
TA: ?
office: ??? PGH
e-mail: ???@cs.uh.edu officehours: ??p
class meets: TU/TH 1-2:30p in 232 PGH
cancelled classes: Tu., Nov. 29. 2005
makeup classes: Friday, Nov. 11, 2-5p (double makeup class for Rita and Nov. 29, 2005)
class room: 232 PGH

Course Materials

Required Text:
Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques
Morgan Kaufman Publishers, 2001, ISBN 1-55860-489-8
Link to Data Mining Book Home Page
Recommended Text
P.-N. Tang, M. Steinback, and V. KumarIntroduction to Data Mining,
Addison Wesley,
Link to Book HomePage

Fall 2005 Teaching Plan (subject to change)

News COSC 6367 (Data Mining) Fall 2005

Prerequisites

The course is mostly self-contained. However, students talking the course should have sound software development skills. Lacking these skills likely will ask for trouble when performing the course projects.

2005 Homeworks, Projects and Assignments

Homework1 (due on Th., Sept. 29 in class)
Homework2 (due on Mo., October 10, 10a --- electronic submission)
Homework3 (due on Tu., Nov. 8, in class)
Homework4 (due on Sa., Dec. 3, 11p, electronic submission)

Course Elements

1 quiz(first week of October), 1 midterm (Tu., November 15, 2005), and 1 final exam
1 paper review
4 assignments/homeworks (contain medium-size KDD projects, short review problems, and tasks some of which might involve some programming or using a KDD-tool; other problems are intended as a prepartion for the exams of the course)

Due Dates and Exam Dates 2005

Homework1: Th., Sept. 27, 2005, in class
Homework2: Mo., October 10, 10a (electronic submission)
Exam1: Th., October 13
Homework3: Tu., November 8, in class
Exam2: Th., November 10, 2005
Homework4: Sa., Dec. 3, 2005
Final Exam: ...

Class Transparencies

I Introduction to Data Mining (Part1, Part2 (covers Chapter1 of the Han book))
II Exploratory Data Analysis (the transparencies are "self-explanatory")
III Data Preprocessing (covers Han Chapter3; finalized set of transparencies to be used in the second week of the semester)
IV Concept Description (Han Chapter5; transparency 40 and higher will not be discussed in the lecture)
V Introduction to Classification: Basic Concepts and Decision Trees (last transparency modified and 4 transparencies added on Sept. 28, 2005)
VI More on Classification: Instance-based Learning and Support Vector Machines (NN-Classifiers and Support Vector Machines (updated on Sept. 28, 2005), Editing and Condensing Techniques for NN-Classifiers)
VII Introduction to Similarity Assessment and Clustering
VIII More on Clustering: Grid-based, Hierarchical and Density-based Clustering (more on AGNES and DBSCAN), Critical Issues with Respect to Clustering", Supervised Clustering.
IX Brief Introduction to Data Cubes (Han Chapter 2 centering on cubes)
X Association Rules
XI Paper Walk Throughs
XII Mining Complex Types of Data Part1, Part2 (covers Han Chapter 9 in part)
XIII Spatial Data Mining (Spatial Databases, Spatial Data Mining (very long), Spatial Data Mining Transparencies covered in class)
XIV Final Words

Remark: Topics with '*' might not be covered due to a lack of time.

2005 Paper Walk Throughs

The paper walk through will take place during the makeup class on Fr., November 11, 2005 from 2-5p. The following 2 papers will be discussed: Original DBSCAN Paper, Adding Data Mining Capabilities to Data Cubes Paper. Everybody should read the two papers carefully. Students are assigned to different Sections of the Paper and should be prepared to summarize the contents of their assigned section and are in charge of directing the discussion of the paper. Students are also assigned as backups and should be prepared to help the student in charge of discussing the paper, and are expected to actively particiapte in the discussion of this section. In the case, that you do not understand something, write down what you do not understand in form of a question. 
Student Assignments for the DBSCAN-Paper:

Section 1+2: Wu, Ma
Section 3: Jefferson, Thomas
Section 4, 4.1: Wadwani, Patel
Section 4.2: Ma, Wu
Section 5: Thomas, Jefferson
Section 6: Patel, Wadwami

Student Assignments for the Sarawawi Paper:

Section 1: Arigai, Belokrylov
Section 2: Alkan, Vaezian
Sections 3.1, 3.2, 3.3: Belokrylov, Arigai
Section 3.4, 3.5, 3.6: Saveran, Tabbaa
Section 4.1: Vaezian, Alkan
Section 4.2, 5: Tabbaa, Saverna

Grading

Each student has to have a weighted average of 74.0 or higher in the exams of the course in order to receive a grade of "B-" or better for the course. Students will be responsible for material covered in the lectures and assigned in the readings. All homeworks and project reports are due at the date specified. No late submissions will be accepted after the due date. This policy will be strictly enforced.

Translation number to letter grades:
A:100-90 A-:90-86 B+:86-82 B:82-77 B-:77-74 C+:74-70
C: 70-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0

Only machine written solutions to homeworks and assignments are accepted (the only exception to this point are figures and complex formulas) in the assignments. Be aware of the fact that our only source of information is what you have turned in. If we are not capable to understand your solution, you will receive a low score. Moreover, students should not throw away returned assignments or tests.

Students may discuss course material and homeworks, but must take special care to discern the difference between collaborating in order to increase understanding of course materials and collaborating on the homework / course project itself. We encourage students to help each other understand course material to clarify the meaning of homework problems or to discuss problem-solving strategies, but it is not permissible for one student to help or be helped by another student in working through homework problems and in the course project. If, in discussing course materials and problems, students believe that their like-mindedness from such discussions could be construed as collaboration on their assignments, students must cite each other, briefly explaining the extent of their collaboration. Any assistance that is not given proper citation may be considered a violation of the Honor Code, and might result in obtaining a grade of F in the course, and in further prosecution.

Course Exams

Quiz

Midterm

Final Exam

Data Mining Links

KDnuggets
KDD 2005 Conference
IEEE International Conference on Data Mining (ICDM) Website
PKDD 2005 (European KDD Conference)
UIUC Data Mining Group
Microsoft DMX Group
Penn Data Mining Group
UMN Spatial Database and Spatial Data Mining Group
Vrije Universiteit Amsterdam Data Mining Group
Data Mining and Machine Learning Group University of Helsinki
Data Mining at Massey University
UH's Data Mining and Machine Learning Group (UH-DMML)