Introduction to Data Mining

Course Description

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)

Textbooks and References

Textbook

  • Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2006, Second Edition.

References

  • Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers.
  • Chakrabarti. Mining the Web: discovering knowledge from hypertext data. Morgan Kaufmann , 2003. Available on line at FIU Library .
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TOPOC (Click to Download PPT)
1
Course Organization, Introduction (Lecture Slides)
2
Ch1:  Data Mining Introduction, Data Pre-processing
3
Weka Introduction  I  (Lecture Slides)
(Please bring your laptop to work in class)
4
Ch2:  Data Introduction (Lecture Slides)
5
Ch2:  Data Similarity (Lecture Slides)
Quiz 1
6
Ch2:  Data Cleaning and Transformation (Lecture Slides)
Quiz 2
7
Ch2:  Data Reduction  (Lecture Slides)
Quiz 3
8
Ch2:  Data Reduction  (Lecture Slides)
9
Ch3: Data Warehouse (Lecture Slides)
10
Ch5: Association Mining (Lecture Slides)
11
Ch5: Association Mining (Lecture Slides)
12
Ch5: Association Mining  (Lecture Slides)
13
Ch5: Association Mining  (Lecture Slides)
Ch8.3: Mining Sequential Paterns (Lecture Slides)
14
Ch8.3: Mining Sequential Paterns (Lecture Slides)
15
Ch6: Classification and Prediction (Lecture Slides)
16
Ch6: Decision Tree Classifer (Lecture Slides)
17
Ch6: Naive Bayes Classifer and Rule Based Classifier(Lecture Slides)
18
Ch6: Nearest Neighbor Classifier, SVM (Lecture Slides)
19
Ch6:  Classifier evaluation and  Ensemble Classifer (Lecture Slides)
20
Ch 7:  Clustering Introduction (Lecture Slides)
21
Ch 7:  Partitional and Hierarchical Clustering (Lecture Slides)
22
Ch7:  Density-based Clustering  (Lecture Slides)

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