Introduction to Data Mining
CAP4770, Fall 2016

Time: Tu/Th 5:00pm - 6:15pm
Place: PG6 114 (map)

Instructor: Ruogu Fang (
    Office: ECS 333, (305)348-7982
    Office Hours: Tu 6:30 pm - 7:30 pm

Teaching Assistant: Tesfagabir Meharizghi (

Data Mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It has gradually matured as a discipline merging ideas from statistics, machine learning, database and etc. This is an introductory course for junior/senior computer science undergraduate students on the topic of Data Mining. Topics include data mining applications, data preparation, data reduction and various data mining techniques (such as association, clustering, classification, anomaly detection)

  • Data Mining Introduction
  • Data Mining Applications
  • Understanding Data
  • Data Preprocessing
  • Data Warehouse
  • Mining Association Rules
  • Classification and Prediction
  • Clustering

Course Schedule

Course Calendar

For lecture notes please access the Moodle System:

Policies on Assignments and Exams

All project deliverables and assignments should be submitted before midnight on the due date. The only excuse for missing an exam is verifiable cases of illness and emergencies and religious holidays. Please check the dates for exams and inform me at the earliest of any conflict due to the above-mentioned reasons.


The course assignments include projects and written homeworks. Projects will be designed to improve the critical analysis and problem-solving skills of students. Class attendance is mandatory. In addition, occasional quizzes will be given in class. Evaluation will be a subjective process, but it will be primarily based on the students' understanding of the course material. Final grades will be calculated as follows.
  • Assignments: 30%
  • Exams: 30%
  • Final Project: 20%
  • Quizzes: 10%
  • Class Participation: 10%

The main textbook for the class:
  • Jiawei Han, Micheline Kamber and Jian Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2011, Third Edition.
  • COP 3530 Data Structures and Algorithms
Academic Integrity

This course follows the Florida International University Code of Academic Integrity. Each student in this course is expected to abide by the Florida International University Code of Academic Integrity. Any work submitted by a student in this course for academic credit must be the student's own work. Violations of the rules will not be tolerated.

2016 Ruogu Fang. All rights reserved. Last Updated: