Data Mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. In this cource we concentrate on the following references:
- J. Han, M. Kamber, J. Pei, "Data Mining, Concepts and Techniques ", Elsevier, 3rd Edition, 2012. Amazon, Quotes, Supplemental Reading.
- P. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Pearson; 2015, Amazon, Authors Page
Download course materials
Reference (Exercise Zero. Crack the password if you can!)
Mid term: 14 November 2017 (23 Aban)
Project announcement: 23 October 2017 (1 Aban)
Project submission: 31 December 2017 (10 Dey)
You can see the archived contents here.
- - Data Mining: why? what? how?
- - Getting to Know Your Data
- - Data Preprocessing
- - Data Warehousing and Online Analytical Processing
- - Data Cube Technology
- - Mining Frequent Patterns, Associations, and Correlations
- - Advanced Pattern Mining
- - Classification: Basic Concepts
- - Classification: Advanced Methods
- - Cluster Analysis: Basic Concepts and Methods
- - Top ten algorithms in Data Mining.