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Data Mining (Master)

Data Mining (Master)

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Course Information

CourseData mining CodeDM1-KMQUT243
Directions70610501 – Artificial Intelligence (Master) Semester1
Type of subjectElective Taught LanguageEnglish
Lectures30 Practical Lessons46
Subject Teacherdr. Jamolbek Mattiev Independent Work104
Total Hours180 Credits6

Lectures – Semester I

CodeTopicMaterial
M1Introductory lecture: Basic concepts and definitionsDownload
M2Areas of application of data mining and machine learning Download
M3The CRISP-DM standardDownload
M4Input conceptsDownload
M5Understanding and visualizing the data in different formatsDownload
M6Introduction to statisticsDownload
M7Data preparation (discretization, normalization, balancing, …)Download
M8Classification (Supervised machine learning techniques) Part I: Majority classifier (ZeroR), one rule classifier (OneR) and Naïve BayesDownload
M9Classification Part II: decision treesDownload
M10Classification Part III: decision trees—C4.5 algorithmDownload
M11Classification Part III: decision rulesDownload
M12Clustering (Unsupervised machine learning techniques): K-means and Hierarchical Agglomerative clustering algorithmsDownload
M13Evaluation and assessment of the learnt modelsDownload
M14Regression and nearest neighborDownload
M15Association Rule Mining (Unsupervised machine learning techniques): frequent pattern mining and association rule discoveryDownload

Practical Lessons – Semester I

CodeTopicMaterial
A1Installing the WEKA software Download
A2Exploring the WEKA workbenchDownload
A3Doing exercises with WekaDownload
A4Downloading the dataset from UCI Machine Learning repositoryDownload
A5Analyzing the input concepts by sample dataset on WekaDownload
A6Using WEKA to load and visualize sample data sets – understanding the ARFF formatDownload
A7Applying some statistical methods on WekaDownload
A8Finding the missing values and outliers using WekaDownload
A9EDA and transforming the data by using WEKADownload
A10Applying simple classification algorithms (ZeroR, oneR, Naive Bayes) to sample data setsDownload
A11Learning decision trees from sample data with WEKADownload
A12Executing the C4.5 algorithm on WekaDownload
A13Executing the decision rules algorithms on WekaDownload
A14Using different clustering methods to describe data. Experiments on WEKA Download
A15Doing exercises on K-means clustering algorithm Download
A16Evaluating and comparing different machine learning algorithms on different datasets in WEKA. Performing statistical significance testing Download
A17Learning cross-validation and percentage split method on Weka Download
A18Linear regression and KNN algorithms on Weka Download
A19Finding frequent patterns and association rules in sample data using WEKA Download
A20classification of association rules on Weka Download
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