Syllabus for Data Mining II

Informationsutvinning II

Syllabus

  • 5 credits
  • Course code: 1DL460
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1F, Technology A1F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2010-03-18
  • Established by:
  • Revised: 2019-11-01
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Spring 2020
  • Entry requirements:

    120 credits including Data Mining I. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course, the student should be able to:

  • explain advanced methods for data mining from large amounts of data, both in theory and in practical application
  • implement and use these methods with appropriate tools and
  • evaluate and compare the suitability, scalability and efficiency of different methods.

Content

Advanced methods in data mining covering the fields of web data mining and search engines, spatial and temporal data mining including cluster evaluation and advanced clustering, stream based data mining, and sequential association analysis. Additional areas include advanced association analysis, methods for large-scale data mining, introduction to outlier analysis, and methods for privacy-preserving in data mining.

Instruction

Lectures, seminars, laboratory sessions and assignments. Guest lecture

Assessment

Written examination (3 HE credits) and written and oral presentation of assignments (2 HE credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

The course can not be counted in higher education qualification together with the course Data Mining (7.5 HE credits).

Syllabus Revisions

Reading list

Reading list

Applies from: Autumn 2020

Some titles may be available electronically through the University library.

  • Introduction to data mining Tan, Pang-Ning; Steinbach, Michael; Karpatne, Anuj; Kumar, Vipin

    Second edition.: Harlow: Pearson Education, 2020

    Find in the library

    Mandatory

Print syllabus and reading list
Print