Data Warehousing/Mining 1

Data Warehousing/Mining

Comp 150DWCourse Overview

Instructor: Dan Hebert

Data Warehousing/Mining 2

Comp 150

Thursday 6:50 - 9:50 PM Instructor - Mr. Dan Hebert

– email - dhebert@mitre.org– Location - Halligan Hall, rm. 108

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Course Description

Fundamental concepts and techniques of data warehousing and data mining– concepts, principles, architecture, design,

implementation, and application of data warehousing and data mining

Topics: Data warehousing and OLAP technology for data mining, data preprocessing, data mining primitives, languages and systems, descriptive data mining, both characterization and comparison, association analysis, classification and prediction, cluster analysis, mining complex types of data, and applications and trends in data mining

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Course Prerequisite

Comp 115 – Introduction to RDBMS– Familiarity with programming with C/C++ is

assumed Students should be comfortable with:

– relational model basics– relational algebra– SQL– Views– Security– conceptual database design and ER models– schema refinement and normal forms– physical database design and tuning

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Required Textbook

Data Mining Concepts and Techniques– Jiawei Han & Micheline Kamber– Morgan Kaufmann Publishers; ISBN: 1-55860-489-8

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Reading Schedule

Lecture Date Topic Reading: Text Chapter January 22 Introduction to Comp 150, Introduction 1 January 29 Data Warehouse and OLAP

Technology for Data Mining 2

February 5 Aggregation in SQL, Data Warehousing Introduction, Data Warehousing Design

Not In Book

February 19 President’s Day Schedule Shift– No Class

February 26

Data Warehouse Semantics Semistructured Data

Not In Book

March 4 Data Preprocessing 3 March 11 Data Mining Primitives, Languages,

and System Architectures – Midterm Review

4

March 18 Midterm Exam

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Reading Schedule (continued)

Lecture Date Topic Reading: Text Chapter

April 1 Concept Description: Characterization and Comparison

5

April 8 Mining Association Rules in Large Databases

6

April 15 Classification and Prediction 7 April 14 Cluster Analysis 8 April 22 Mining Complex Types of Data 9 April 29 Applications and Trends in Data

Mining – Final Exam Review 10

May 6 Reading Period/Project Completion May 13 Final Exam

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Grading

Homework 30% Project 10% Midterm 25% Final 35%

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Homework

Assigned weekly (each Wednesday) – Due at the start of lecture the following

Wednesday Late policy:

– Homework turned in up to one week after the due date - 20% penalty.

– Homework turned in anytime later - 100% penalty

Typical homework assignment– Exercises from the text– “Hands-on” problems that involve building data

warehouses and performing data mining Working with PostgresQL

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Project

Develop a data warehouse and perform data mining on it using Postgres as the underlying datastore

Additional details provided as the course progresses

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Midterm & Final

Open book, open notes Opportunity during class for review

of material covered prior to midterm and final

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Computing Environment

All students will have a computer account on psql.cs.tufts.edu– Account will work on all workstations

in the SUN lab Commercial RDBMS utilized will be

PostgreSQL– For information -

http://www.postgresql.org/index.html

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Course Homepage

Course web page will be available Lectures/homework assignments

will also be posted in my account– ~dhebert/comp150dw