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Week - 1 |
Introduction to data mining, fundamental concepts of data mining, knowledge discovery processes, application areas of data mining, and current research problems. |
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Week - 2 |
Data types and datasets, attribute types, statistical properties of data, similarity and distance measures, and data visualization techniques. |
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Week - 3 |
Data preprocessing methods, missing data analysis, noisy data cleaning, data transformation, normalization, and data integration. |
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Week - 4 |
Data reduction techniques, feature selection, dimensionality reduction, PCA method, sampling techniques, and data compression approaches. |
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Week - 5 |
Association rule mining, frequent pattern analysis, support and confidence concepts, Apriori algorithm, and its applications. |
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Week - 6 |
FP-Growth algorithm, closed and maximal patterns, pattern evaluation methods, and advanced pattern mining approaches. |
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Week - 7 |
Introduction to classification, decision trees, information gain, gain ratio, Gini index, and decision tree construction methods. |
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Week - 8 |
Bayesian classifiers, Naive Bayes algorithm, K-Nearest Neighbor method, model evaluation, and performance metrics. |
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Week - 9 |
Introduction to cluster analysis, similarity measures, K-Means algorithm, K-Medoid method, and clustering performance evaluation. |
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Week - 10 |
Hierarchical clustering methods, density-based clustering, DBSCAN algorithm, and clustering in high-dimensional datasets. |
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Week - 11 |
Introduction to text mining, text preprocessing, tokenization, stop-word removal, stemming, and TF-IDF methods. |
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Week - 12 |
Text classification methods, document similarity, sentiment analysis, information retrieval systems, and natural language processing applications. |
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Week - 13 |
Big data analytics, social network analysis, web mining, recommender systems, and current research topics in data mining. |
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Week - 14 |
Semester project presentations, academic paper discussions, current research trends, and overall course evaluation. |
Eskisehir Technical University Info Package