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Database and Data Mining

Database and Data Mining

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Course Information

Course Database and data mining II (2-semester) Code MBDM 1-3 14
Directions 70230801 – Computer Linguistics (Master) Semester 1, 2
Type of subject Elective Taught Language English
Lectures 40 Practical Lessons 80
Subject Teacher dr. Jamolbek Mattiev Independent Work 180
Total Hours 300 Credits 10

Lectures – Semester I

Code Topic Material
M1 Random split method Download
M2 Classification Part II: decision trees with information gain ratio and gini index methods Download
M3 Classification Part III: decision trees—C4.5 algorithm Download
M4 Classification Part IV: rule-based classification models Download
M5 Clustering (Partitional clustering techniques): K-Medoids clustering algorithm Download
M6 Clustering (Hierarchical clustering techniques): Hierarchical Agglomerative clustering algorithms Download
M7 Determining the number of clusters Download
M8 Evaluation and assessment of the learnt models Download
M9 Association Rule Mining (Supervised machine learning techniques): frequent pattern mining and association rule discovery. APRIORI algorithm Download
M10 Association Rule Mining (Supervised machine learning techniques): frequent pattern mining and association rule discovery. FP-Growth algorithm Download

Practical Lessons – Semester I

Code Topic Material
A1 Doing exercises on 10-times random-split method with WEKA Download
A2 Classification Part II: decision trees with information gain method Download
A3 Learning decision trees from sample data with WEKA (Information Gain) Download
A4 Learning decision trees from sample data with WEKA (Gini Index) Download
A5 Executing the C4.5 algorithm on WEKA Download
A6 Classification Part III: decision rules Download
A7 Executing the decision rules algorithms on WEKA Download
A8 Executing the rule-based classification algorithms on WEKA Download
A9 Clustering (Partitional clustering techniques): K-means clustering algorithm Download
A10 Using different partitional clustering methods to describe data. Experiments on WEKA (K-means) Download
A11 Using different partitional clustering methods to describe data. Experiments on WEKA (K-medoids) Download
A12 Using different partitional clustering methods to describe data. Experiments on WEKA (Hierarchical Clustering) Download
A13 Finding the optimal number of clusters by using the data. Experiments on WEKA (Hierarchical Clustering) Download
A14 Evaluating and comparing different machine learning algorithms on different datasets in WEKA. Performing statistical significance testing Download
A15 Regression and nearest neighbor Download
A16 Linear regression and KNN algorithms on WEKA Download
A17 Finding frequent patterns and association rules in sample data using WEKA (Apriori) Download
A18 Finding frequent patterns and association rules in sample data using WEKA (FP-Growth) Download
A19 Association Rule Mining (Supervised machine learning techniques): Generation of association rules Download
A20 Generating the association rules and association rules in sample data using WEKA Download
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