SubjectsSubjects(version: 875)
Course, academic year 2020/2021
  
Data Mining - NDBI023
Title: Dobývání znalostí
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2020
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: English, Czech
Teaching methods: full-time
Guarantor: doc. RNDr. Iveta Mrázová, CSc.
RNDr. František Mráz, CSc.
Class: Informatika Mgr. - Teoretická informatika
Informatika Mgr. - Softwarové systémy
Classification: Informatics > Database Systems, Theoretical Computer Science
Annotation -
Last update: RNDr. Jan Hric (28.05.2020)
A rapid development in the area of data mining is motivated by the necessity to "translate" huge amounts of processed and stored data into meaningful information easy to use in practice. This lecture is focused on understanding principal concepts and techniques applicable to data mining. Basic principles of their application to novel solutions of practical tasks will be used to solve a student project as a part of the subject. Possible application areas comprise mainly business and Web applications, but others as well. Knowledge BSc. in CS of mathematical principles and programming is assumed.
Aim of the course -
Last update: doc. RNDr. Iveta Mrázová, CSc. (27.05.2020)

Understand the main principles of data mining methods and learn how to apply these methods in practice.

Course completion requirements -
Last update: doc. RNDr. Iveta Mrázová, CSc. (27.05.2020)

A) The labs

Step by step, in an accompanying Moodle course there will be published assignments and quizzes.

Assignments:

Each assignment has a deadline till which the assignment should be submitted for grading. A draft solution of an assignment can be edited at any time, but the time of submission is the time you click the button "Submit assignment". After clicking this button you cannot edit your submission anymore, but you can ask (per e-mail) your teacher to return the assignment back into the draft state. Each submitted assignment will be graded by the teacher with 0-10 points. During the semester, you will solve 3 assignments.

A typical solution for an assignment will consist of a text – a description of the solution – and a code of a MATLAB program/script used for solving the assignment. Submit your texts as a PDF-file or alternatively as an RTF-file. The source codes should be submitted as plain ASCII files.

Warning: If N≥2 participants of the course will submit solutions which are very similar or identical, all these solutions will be considered as a single solution. The solution will be graded by B points according to its quality and all students who submitted it will obtain only the integer part of the value B/N points.

Quizzes:

Beside the assignments, you will solve several on-line quizzes. For the first so-called entrance test you can obtain at most 10 points. During the term there will be assigned several short tests for at most 10 points altogether. Each quiz will have set up also a deadline. In contrast to assignments it will be not possible to solve any quiz after its deadline.

For obtaining credits for the seminar it is necessary:

  1. To solve all assignments and to obtain at least 1 point for each solution. WARNING: a late submission of a solution will be penalized by 2 points decrease for each started week of the delay after the deadline.
  2. To prepare and to present a term project in the lab in the last week of the term or on a date (during the following exam period) which will be set-up in the lab within the last week of the semester. Subject for the project will be discussed in the lab in the middle of the term. Each project will be graded up to 15 points according to its quality.

The quizzes are not among the necessary conditions for obtaining credits for the lab. During seminars it is possible to obtain additional points

  • for demonstrating a solution of a problem assigned during a seminar – 1 point,
  • for demonstrating a solution submitted as a solutions for an assignment in Moodle (after its deadline) – the integer part of the half of the number of point awarded for the solution (after evaluating by the teacher).

Except for the additional points, it is possible to obtain up to 65 points. All points obtained during seminars will be accounted for up to 40% of the final score of the exam. However, when a student will obtain more than 65 points within labs (counting also additional points), these points will still account for only 40% of the final score of the exam.

Continuous work throughout the whole term is required to obtain the credits, therefore there will be no additional possibilities to acquire them later.

B) The lecture

The lecture will be given once a week according to the schedule. As already mentioned above, points acquired within the labs will account for up to 40% of the final score for the exam. Further, there will be one written test which will be written during a lecture. The date for the written test will be set up within the first week of the semester and published in the accompanying Moodle course.

This test will be graded with 0-15% towards the final score. The exam at the end of this term will add up to the remaining 45% to the final score. The following table gives the final grade according to achieved score:

grade 1 grade 2 grade 3 failure
100%–86% 85%–71% 70%–56% less than 56%

Literature -
Last update: doc. RNDr. Iveta Mrázová, CSc. (27.05.2020)

  1. Aggarwal C. C.: Data Mining: The Textbook, Springer, 2015
  2. Berka P.: Dobývání znalostí z databází, Academia, 2003
  3. Liu B.: Web Data Mining, Springer, 2007
  4. Murphy K. P.: Machine Learning: A Probabilistic Perspective, The MIT Press, 2012

Requirements to the exam -
Last update: doc. RNDr. Iveta Mrázová, CSc. (27.05.2020)

The exam consists of a written and oral part. The written part precedes the oral part. Failing the written part impacts failing the whole exam, i.e. the exam will be classified by the grade 4 (failed) and the exam will not continue with the oral part. When failing the oral part the next (reparative) attempt will consist again of both the written and oral parts. The final grade of the exam is set based on points awarded for the written and oral parts of the exam as well as on the points obtained for student’s work throughout the semester - see Course completion requirements.

The written part of the exam consists of three questions related to the syllabus of the lecture and/or material covered during the lab classes.

The requirements for the exam correspond to the syllabus of the lecture within the extent presented in the classes. In order to take part in the exam, it is necessary to obtain Final course credit.

Syllabus -
Last update: doc. RNDr. Iveta Mrázová, CSc. (27.05.2020)

  1. Introduction to the area of data mining

    • Motivation for data mining and its importance for practice, an overview of frequent data mining tasks, main data mining methodologies.
    • Main principles of machine learning – supervised training, self-organization, semi-supervised learning, training set, test set and validation set, generalization and overfitting, Occam´s razor.

  2. Fundamental paradigms of the data mining process

    • Data gathering, preparation and preprocessing – sampling, variability and confidence, discretization of numeric attributes and handling nonnumerical variables, replacement of missing and empty values, series variables.
    • Transformation, reduction and cleaning of the data – relationships among the attributes (similarity measures, hypothesis testing, correlation, regression, discriminant and cluster analysis), dimensionality reduction.
    • Validation of the obtained results – cross-validation, overall accuracy, confusion matrix, learning curve, lift curve, ROC curve, combination of models (bagging, boosting).

  3. Techniques for association rule mining

    • Market basket analysis – frequent itemsets, association rules, their formulation and main characteristics.
    • Generation of frequent item combinations – algorithm apriori, "frequent-pattern-growth"-techniques (FP-Growth and TD-FP-Growth), combinational data analysis.
    • Constraint-based search for interesting rules (specification of time, items, etc.).

  4. Methods for cluster analysis

    • The k-means algorithm, the choice of a suitable metric, evaluation of the obtained results (cluster validity), representation and visualization of the found clusters.
    • Clustering based on the fuzzy set approach (FCM-clustering), neural approach and hierarchical clustering.
    • Advanced concepts – scalable techniques (CLARANS, BIRCH, CURE), outlier analysis.

  5. Approaches to data classification and prediction

    • Decision trees and their induction – algorithms ID3, C4.5, CART and CHAID.
    • Probabilistic classifiers – Bayessian models and techniques for their training and inference.
    • Nature-inspired models – artificial neural networks of the perceptron type, SVM-machines, ELM-networks, genetic algorithms.

 
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