Advanced Data Mining


Content, progress and pedagogy of the module

Disclaimer. This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

The purpose of the project module is for students to gain knowledge, skills, and competences in relation to the selection and use of advanced data mining techniques in relation to specific application scenarios for the purpose of extracting insights from large scale, heterogenous, complex, and unstructured data

Data mining refers to the process of analyzing large data sets (big data) to extract or discover patterns useful in the contexts of a particular application scenarios. Data mining can make use of statistical learning and statistical methods in general. Applications of data mining span domains such as retail, entertainment, media, Web, manufacturing, and IoT. For example, purchase data collected in retail can be mined to understand customer purchasing patterns that may be utilized for advertising

Learning objectives


  • demonstrate knowledge of the functioning and pertinent properties of main data mining techniques, including techniques that target unstructured data and semi-structured data 

  • demonstrate knowledge of the applicability of data mining techniques in relation to different types of data and use cases 

  • Demonstrate knowledge of the big data technologies and their impact on scalability 

  • Demonstrate knowledge of the methods and techniques required to measure and validate the quality and reliability of the results produced by the various data mining techniques


  • find and pre-process datasets in order to employ data mining techniques to solve a specific data analysis problem 

  • choose and apply appropriate data mining techniques to extract relevant insight from datasets within a given application scenario 

  • combine different data mining techniques in novel ways to solve a realistic application scenario 

  • carry out a systematic evaluation of data mining techniques 

  • understand and utilize Big Data technologies and methods to apply data mining approaches to large datasets 


  • identify possible alternative solutions employing relevant data mining techniques to a given application scenario and argument their possible advantages and disadvantages 

  • reflect on the solutions and methods used

Type of instruction

Project work

Extent and expected workload

It is expected that the student uses 30 hours per ECTS, which for this activity means 450 hours



Name of examAdvanced Data Mining
Type of exam
Oral exam based on a project
Assessment7-point grading scale
Type of gradingExternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Additional information

Contact: The Study board for Computer Science at or 9940 8854

Facts about the module

Danish titleAvanceret data mining
Module codeDSNDVB421
Module typeProject
Duration1 semester
Language of instructionDanish and English
Location of the lectureCampus Aalborg
Responsible for the module


Education ownerBachelor of Science (BSc) in Data Science and Machine Learning
Study BoardStudy Board of Computer Science
DepartmentDepartment of Computer Science
FacultyThe Technical Faculty of IT and Design