Data Mining

Subject description

The course will be centred about the selected topics from the following research areas:

– data pre-processing, outlier detection, feature construction, discretization,

– feature subset selection,

– explorative data analysis, visualization, intelligent visualization techniques,

– predictive modelling (classification and regression) with emphasis on representative and state of the art techniques (Bayesian modelling, support vector machines, rule-based modelling),

– fundamentals of clustering techniques (hierarchical, k-means),

– association analysis,

– model evaluation and scoring,

– industrial, scientific, and business applications of data mining, fundamentals of text and web-mining,

– data mining tools, with emphasis on script-based approaches and visual programming frameworks.

The subject is taught in programs

Objectives and competences

Students will become familiar with fundamental and advanced concepts in data mining, and will learn how to apply data mining to solve complex real-life problems. They will be able to apply the right set of pre-processing techniques, use visualization for exploratory analysis, select the right modelling technique and present the constructed model in the form that could reveal new patterns and knowledge to domain experts. During lab work they will learn how to use state-of-the art data mining tools.

Teaching and learning methods

Lectures supported by audio-visual equipment and appropriate hardware and software. Active use of the system for management of teaching material (e.g., Moodle). Combination of group-based and individual studies, accompanied with student/teacher interactions at seminars and consulting hours.

Expected study results

Knowledge and understanding: Knowledge and understanding of data mining methods, ability to use them and evaluate the results.

Application: Application on real-world data.

Reflection: Understanding the relation between the theoretical aspects and practical use of the methods.

Basic sources and literature

Tan P-N, Steinbach M, Kumar V: Introduction to data mining, Pearson Addison Wesley, 2005.

Dodatna: Hastie T, Tibshirani R, Friedman J: The elements of statistical learning: data mining, inference and prediction, Springer, 2001.

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University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana

E: T:  01 4768 411