| Abbreviation: RUDPOD
|
Load: 30(L)
+ 15(E)
+ 0(LE)
+ 0(S)
+ 0(FLE)
+ 0(PEE)
|
| Lecturers in charge: |
dr. sc. Tonči Carić |
| Lecturers: |
prof. dr. sc. Hrvoje Gold
(
Lectures
)
pred. dr. sc. Krešimir Vidović
(
Lectures
)
Martina Erdelić mag. ing. traff.
(
Exercises
)
Leo Tišljarić
(
Exercises
)
|
| Course description: Basic terms and definitions. The need and motivation for using data mining. Basic data mining functions. Data pre-processing
and post-processing, dimensionality reduction and data transformation. Data visualization. Data classification. Associative
analysis. Cluster analysis. Anomaly detection. Applying data mining methods to traffic system databases. Traffic forecasting.
Providing additional services to users of the traffic system based on data mining results. Application of data mining results
in optimization and management in the transport system.
|
| Lecture languages: en, hr
|
| Compulsory literature: |
| 1. |
Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Addison Wesley, 2005. |
| 2. |
Šemanjski, Ivana: Rudarenje podataka, nastavni materijali, Fakultet prometnih znanosti, Sveučilište u Zagrebu, 2017. |
| Recommended literature: |
| 3. |
Palm, William: Introduction to MATLAB for Engineers, McGraw-Hill Education, 2010. |
| 4. |
Gilat, Amos: MATLAB: An Introduction with Applications, Wiley, 2014. |
| 5. |
Ng, Andrew: Machine learning Yearning, Mlyearning, 2017. |
| 6. |
Zheng, Alice: Feature Engineering for Machine Learning Models: Principles and Techniques for Data Scientists, O'Riley, 2017. |
| 7. |
Kirk, Andy: Data Visualisation: A Handbook for Data Driven Design, SAGE Publications Ltd, 2016. |
|