FID3016 Data Mining 7.5 credits

Data mining

The course studies fundamentals of data mining, data stream processing, and machine learning algorithms for analyzing very large amounts of data. We will use big data processing platforms, such as MapReduce, Spark and Apache Flink, for implementing parallel algorithms, as well as computation systems for data stream processing, such as Storm and InfoSphere. The course also considers current research topics in data mining with a focus on mining of very large amounts of data.

Offering and execution

Course offering missing for current semester as well as for previous and coming semesters

Course information

Content and learning outcomes

Course contents *

The course contents includes:
• Introduction to Data Mining
• Frequent Itemsets
• Finding Similar Items
• Clustering
• Recommendation Systems
• Mining Data Streams
• Dimensionality Reduction
• Large-Scale Machine Learning
• Overview of the current research in data mining and its connection to other relevant research areas.

Intended learning outcomes *

The course studies fundamentals of data mining, data stream processing, and machine learning algorithms for analyzing very large amounts of data. We will use big data processing platforms, such as MapReduce, Spark and Apache Flink, for implementing parallel algorithms, as well as computation systems for data stream processing, such as Storm and InfoSphere. The course also considers current research topics in data mining with a focus on mining of very large amounts of data.
After this course, students will be able to mine different types of data, e.g., high-dimensional data, graph data, and infinite/never-ending data (data streams); as well as to program and build data-mining applications. They are also expected to know how to solve problems in real-world applications, e.g., recommender systems, association rules, link analysis, and duplicate detection. They will master various mathemathical techniques, e.g., linear algebra, optimization, and dynamic programming. Moreover, students should be able to describe and apply current research trends in data mining (including methods, algorithms, language support and tools).

Course Disposition

No information inserted

Literature and preparations

Specific prerequisites *

Recommended prerequisites:
Acquaintance with concepts and terminology associated with statistics, database systems, and machine learning; a course on data structures, algorithms, and discrete math (such as ID1020 Algorithms and Data Structures); a course in software systems, software engineering, and programming languages; a course on processing, storing and analyzing massive data (such as ID2221 Data-Intensive Computing).

Recommended prerequisites

Recommended prerequisites:
Acquaintance with concepts and terminology associated with statistics, database systems, and machine learning; a course on data structures, algorithms, and discrete math (such as ID1020 Algorithms and Data Structures); a course in software systems, software engineering, and programming languages; a course on processing, storing and analyzing massive data (such as ID2221 Data-Intensive Computing).

Equipment

No information inserted

Literature

The contents of the course are derived from the following textbook:
A. Rajaraman and J.  D. Ullman, Mining of massive datasets.  Cambridge University Press, 2012 (alternative: J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, 3-rd Ed., Morgan Kaufmann, 2012)

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale *

P, F

Examination *

    Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

    The examiner may apply another examination format when re-examining individual students.

    Other requirements for final grade *

    Approved written examination, approved assignments, and approved application of current research (in the form of using it for a research paper, report, or project, etc).

    Opportunity to complete the requirements via supplementary examination

    No information inserted

    Opportunity to raise an approved grade via renewed examination

    No information inserted

    Examiner

    Vladimir Vlassov

    Ethical approach *

    • All members of a group are responsible for the group's work.
    • In any assessment, every student shall honestly disclose any help received and sources used.
    • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

    Further information

    Course web

    Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

    Course web FID3016

    Offered by

    EECS/Software and Computer Systems

    Main field of study *

    No information inserted

    Education cycle *

    Third cycle

    Add-on studies

    No information inserted

    Postgraduate course

    Postgraduate courses at EECS/Software and Computer Systems