Meysam Madani

داده کاوی پردازی - میثم مدنی

با توجه به مخالفت اغلب دوستان با تاریخ ۵ و ۷ آذر، همچنین تعداد کم مخالف با ۲۳ آبان، امتحان روی همان زمان قبلی خواهد ماند، یعنی روز ۲۳ آبان

امتحان از ابتدا تا انتهای درخت رشد الگو خواهد آمد، البته فصل‌های ۴ و ۵ هم حذف هستند.

داده کاوی پردازی - میثم مدنی

Data Mining

Data Mining 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.

Data Mining

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:

References

  1. J. Han, M. Kamber, J. Pei, "Data Mining, Concepts and Techniques ", Elsevier, 3rd Edition, 2012. Amazon, Quotes, Supplemental Reading.
  2. 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!)

Slides: | Chapter 1 | Chapter 2 | Chapter 3 | Chapter 4 | Chapter 5 | Chapter 6 | Chapter 7 | Chapter 8 |

Important dates:

Mid term: 14 November 2017 (23 Aban)

Project announcement: 23 October  2017 (1 Aban)

Project submission31 December 2017 (10 Dey)


You can see the archived contents here.



Course outline:

  1. - Data Mining: why? what? how?
  2. - Getting to Know Your Data
  3. - Data Preprocessing
  4. - Data Warehousing and Online Analytical Processing
  5. - Data Cube Technology
  6. - Mining Frequent Patterns, Associations, and Correlations
  7. - Advanced Pattern Mining
  8. - Classification: Basic Concepts
  9. - Classification: Advanced Methods
  10. - Cluster Analysis: Basic Concepts and Methods
  11. - Top ten algorithms in Data Mining.