Outline of CPIT440

Course Objectives

Upon completion of this course, students should be able to:

1.*Design and implement data mining solution to a given problem,

2.Use numerical and graphical methods to summarize data,

3.*Identify and remove missing values, noise and outliers in presented data ,

4.Identify relationships in presented data and Measuring Data Similarity and Dissimilarity,

5.Differentiate between data warehouse and operational database,

6.*Design a data warehouse schema using fact and dimension tables,

7.Describe the OLAP operations to analyze data in a data cube,

8.*Use frequent pattern mining methods to extract association rules from given data,

9.Characterize and rank the association rules using support, confidence and lift,

10.*Use classification to build prediction models,

11.Validate a prediction model-using split and cross validation methods,

12.Compare and contrast classifiers using various evaluation measures,

13.Differentiate between hierarchical and partitioning clustering methods,

14.Use clustering algorithms to form clusters in given data.

Learning Resources

• ✍Jiawei Han, Micheline Kamber and Jian Pei, Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, Third Edition, 2012.

  http://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf

• ✍Pang-Ning Tan, Michael Steinback, and Vipin Kumar, Introduction to Data Mining, Addison Wesley, 2006.

  http://www-users.cs.umn.edu/~kumar/dmbook/index.php

• ✍Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow:Concepts, Tools, and Techniques to Build Intelligent Systems”, Aurélien Géron,O'Reilly Media; 2 edition (August 4, 2019)

• ✍Students will be given slides suitable to the material given in each lecture.
• ✍The students are expected to learn through attendance at lectures, solving book problems,participating in the general discussions, and through reading materials found in the recommended textbooks.

Course Requirements and Grading

Your performance in this course will be evaluated in three areas: three assignments, lab activity and three exams: