Cluster analysis algorithms in applications Field of study: Computer Science
Programme code: W4-S2INA19.2021

Module name: Cluster analysis algorithms in applications
Module code: W4-INA-S2-20-F-AASwP
Programme code: W4-S2INA19.2021
Semester:
  • summer semester 2022/2023
  • winter semester 2022/2023
  • summer semester 2021/2022
Language of instruction: English
Form of verification: course work
ECTS credits: 4
Description:
The goal is to introduce the listener to cluster analysis algorithms: division, hierarchical, density or new cluster analysis algorithms. The students will also consider their practical uses in medicine.
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]
The student is aware of grouping algorithms' advantages and their impact on learning the analysed data and their fields. [M_001]
 K_K02 [1/5]
The student knows the basics of data mining, including data types, similarity measures, and methods for determining cluster representatives. [M_002]
 K_W01 [2/5] K_W02 [2/5] K_W04 [2/5] K_W09 [3/5]
The student knows partitioning grouping algorithms, including k-means and k-medoids. [M_003]
 K_W04 [2/5] K_W09 [3/5]
The student knows hierarchical grouping algorithms, including AHC. [M_004]
 K_W04 [2/5] K_W09 [3/5]
The student knows density grouping algorithms, including DBSCAN. [M_005]
 K_W02 [2/5] K_W04 [2/5] K_W09 [3/5]
The student 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]
The student can implement or use ready-made libraries/packages that allow a split algorithm for any actual data set. [M_007]
 K_U01 [1/5] K_U03 [2/5] K_U08 [2/5] K_U09 [3/5]
The student can implement or use ready-made libraries/packages that allow a hierarchical algorithm for any actual data set. [M_008]
 K_U01 [1/5] K_U03 [2/5] K_U08 [2/5] K_U09 [3/5]
The student can implement or use ready-made libraries/packages that allow a density algorithm for any actual data set. [M_009]
 K_U01 [1/5] K_U03 [2/5] K_U08 [2/5] K_U09 [3/5]
The student can appoint a representative of a group of objects in the multidimensional space. [M_010]
 K_U01 [2/5] K_U03 [3/5] K_U08 [2/5] K_U09 [4/5]
The student can visualise 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]
The test checks how the students internalised content presented during the lectures. The exam comprises both open and closed-ended 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]
The students will develop projects with reports within deadlines to verify the skills gained 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]
The lectures are based on audiovisual aids with the additional use of some written educational ones.
 15
Preparation for the exam.
 15 exam (test) [W_001]
laboratory classes [Z_002]
The students prepare for solving tasks individually by studying the proceeding method and the sequence of operations.
 30
Classes prepare the students for completing the assigned tasks individually during the laboratory class. They are also required to elaborate on 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)