Cluster analysis algorithms in applications Field of study: Computer Science
Programme code: W4-S2IN19.2022

Module name: cluster analysis algorithms in applications
Module code: W4-IN-S2-20-F-AASwP
Programme code: W4-S2IN19.2022
Semester:
  • summer semester 2025/2026
  • winter semester 2025/2026
  • summer semester 2024/2025
  • winter semester 2024/2025
  • summer semester 2023/2024
  • winter semester 2023/2024
Language of instruction: Polish
Form of verification: course work
ECTS credits: 4
Description:
The goal is to introduce the listener to cluster analysis algorithms, both division, hierarchical, density and new cluster analysis algorithms. Their practical use in medicine will be considered.
Prerequisites:
(no information given)
Key reading:
Guojun Gan, Chaoqun Ma, Jianhong Wu, „Data Clustering: Theory, Algorithms, and Applications”, 2007, SIAM Brian S. Everitt, Sabine Landau, Morven Leese, Daniel Stahl, „Cluster Analysis, Copyright 2011 John Wiley & Sons JOHN A. HARTIGAN, „Clustering Algorithms „ Department of Statisties Yaie University , 1975, John Wiley & Sons. Tadeusz Morzy , „Eksploracja danych : metody i algorytmy”, Warszawa, 2013, PWN Wierzchoń Sławomir , Kłopotek Mieczysław , „Algorytmy analizy skupień”, PWN , 2017 Gatnar Eugeniusz, Walesiak Marek, „Statystyczna analiza danych z wykorzystaniem programu R”, PWN, 2009
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
Is aware of the advantages of grouping algorithms and their impact on learning the analyzed data and their fields. [M_001]
 K_K02 [1/5]
Has knowledge of the basics of data mining, including data types, measures of similarity, methods for determining cluster representatives [M_002]
 K_W01 [2/5] K_W02 [2/5] K_W04 [2/5] K_W09 [3/5]
Has knowledge of partitioning grouping algorithms, including k-means and k-medoids [M_003]
 K_W04 [2/5] K_W09 [3/5]
Has knowledge of hierarchical grouping algorithms including AHC [M_004]
 K_W04 [2/5] K_W09 [3/5]
Has knowledge of the density grouping algorithms including DBSCAN [M_005]
 K_W02 [2/5] K_W04 [2/5] K_W09 [3/5]
Can determine the similarity / distance of objects relative to each other in multidimensional space [M_006]
 K_U01 [2/5] K_U03 [2/5] K_U08 [2/5] K_U09 [3/5]
Is able to implement or use ready-made libraries / packages that allow the use of a split algorithm for any real data set [M_007]
 K_U01 [1/5] K_U03 [2/5] K_U08 [2/5] K_U09 [3/5]
Is able to implement or use ready-made libraries / packages that allow the use of a hierarchical algorithm for any real data set [M_008]
 K_U01 [1/5] K_U03 [2/5] K_U08 [2/5] K_U09 [3/5]
Is able to implement or use ready-made libraries / packages that allow the use of a density algorithm for any real data set [M_009]
 K_U01 [1/5] K_U03 [2/5] K_U08 [2/5] K_U09 [3/5]
Is able to appoint a representative of a group of objects in multidimensional space [M_010]
 K_U01 [2/5] K_U03 [3/5] K_U08 [2/5] K_U09 [4/5]
Can visualize the received structure of groups and interpret it correctly [M_011]
 K_U01 [1/5] K_U03 [2/5] K_U09 [3/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
exam (test) [W_001]
Knowledge verification based on the content presented in the lecture. The exam consists of both open and closed theory questions.
 M_001 M_002 M_003 M_004 M_005 M_006 M_007 M_008 M_009 M_010 M_011
Projects and reports [W_002]
Developing projects with reports for them within a specified period as a verification of the skills acquired in solving problems.
 M_001 M_006 M_007 M_008 M_009 M_010 M_011
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [Z_001]
Transferring the content of education in verbal form using audiovisual means and other written teaching aids.
 15
Preparation for the exam.
 15 exam (test) [W_001]
laboratory classes [Z_002]
Detailed preparation of students to solve tasks with an indication of the methodology of the procedure, an indication of the order of performed activities.
 30
Preparation for the laboratory. Student's independent solution of tasks assigned to the laboratory, preparation of reports
 60 Projects and reports [W_002]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)