|Website:||website containing additional information|
|Period:||period 1 (week 36 through 45, i.e., 4-9-2017 through 10-11-2017; retake week 1)
|Participants:||up till now 107 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|Contents:||Note: basic knowledge of probability, statistics and calculus is
Also, you should be able to write a program, but experience with the R language is not required.
The following subjects are discussed:
- Classification Tree Algorithms, Bagging and Random Forests
- Graphical Models (including Bayesian Networks)
- Frequent Pattern Mining
- Text Mining
- Social Network Mining
|Literature:||Lecture notes and selected articles/book chapters.
|Course form:||Lectures and Computer Lab.|
|Exam form:||Written exam and practical assignments.|
|Minimum effort to qualify for 2nd chance exam:|| |
The amount of data that is produced and stored by companies and other organizations is still growing every day.
In addition, the amount of information available on the web/social media is growing fast. If properly processed and analyzed, this data can be a valuable source of knowledge.
Data mining provides the theory, techniques and tools to extract knowledge from data.
Examples of problems that data mining can help address are:
Learning models from data can also be an important part of building a
decision support system. In turn, the computer plays an increasingly important
role in data analysis:
through the use of computers, computationally expensive data mining
methods can be applied that were not even considered in the early days of
statistical data analysis.
- Identify the risk factors for prostate cancer on the basis of clinical and demographic variables.
- Make a segmentation into groups of similar customers based on
their characteristics and purchase bahaviour.
- Determine which products are frequently bought together in one transaction by customers of a supermarket or web shop.
- Predict whether two people on a social network site will become friends.
In this course we study a number of well-known data mining algorithms.
We discuss what type of problems they are suited for, their computational
complexity and how to interpret and apply the models constructed with them.