Skip to content

Course: Introduction to Data Mining in UIUC Time: 2018/08/27 - 2018/12/12

Notifications You must be signed in to change notification settings

laylalaisy/UIUC_1_1_CS412_DataMining

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

48 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

UIUC_1_1_Introduction to Data Minig

You are allowed to read all the codes and files, but you are not allowed to copy directly for your assignments!!!!!!! I will not take any responsibility if you break the honor rule.
Please try yourself and have fun working on those interesting projects!

Courese Information:

  1. Course: ECE 412 An introduction to Data Warehousing and Data Mining at UIUC
  2. Time: 2018/08/271- 2018/12/15
  3. Teacher: Qi Yu (Honestly, I'm not quite recommend this course if you are not really interested in Data Science as me. I chose this course because my interest field is Data Science. Thus, all my course are basically Data Mining/Statistic Inference those kind of course. I only went to this course for the first month, then I just watched all video about twice a month at a 1.5 speed. The speed of professor is tooooo low for me that I always want to sleep and can't focus on the class. But the grading is not bad, a lot of bonus question for exam and the bonus project as 10 points for the total grade is not hard at all. I spent about 3 hours to finish this project. In conclusion, if you want to learn something interesting, not recommend. If you like Data Science, good course as foundation

Program Infromation:

  1. Linux + Ubuntu 16.04
  2. See each program/homework under different folders. I will not upload any resources from professor, I will only upload my work including homework, source code and related notes.

HW

  1. HW1: Statistical Properties + Normalize Data + Relationship between data
  2. HW2: Aprior + FP Tree
  3. HW5: Classification + Clustering

MP

  1. HW3: frequent pattern minig algorithm( Ariori algorithm + FP-Growth) + closed pattern mining algorithm + maximal pattern mining algorithm
  2. HW4: Decision Tree (gini-index) + Random Forest (randomly choose subset of attributes and then majority voting)
  3. HWBonus: similar to hw3 (consider the sequential and performance)

Tips:

  1. All codes and notes will be open source after my final test (in case of copy)
  2. You are allowed to read all the codes and files, but you are not allowed to copy directly for your assignments.
  3. If you have any problems or ideas want to share with me, please feel free to e-mail to me: [email protected]

About

Course: Introduction to Data Mining in UIUC Time: 2018/08/27 - 2018/12/12

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published