This course covers data mining topics. Students will learn some theories and its algorithms. They also work on data mining project. All of the assignments and project are submitted via kulino (https://kulino.dinus.ac.id).

Course Contents:

1. Introduction to Data Mining: KDD Process, Atributes vs Label, Type of attributes, Data normalization, Univariate vs multivariate, Similarity/Dissimilarity, Supervised vs Unsupervised
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2. Preprocessing: Outlier removal and handling missing value
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3. Discretization Techniques: Statistical approach, Chimerged
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   • Assignment #1
4. Classification: K-Nearest Neighbor, Naive Bayes, Decision Tree
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5. Prediction: Corelation, Linear regression
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6. Feature Reduction: Filter, Wrapper, Transformation, Correlation-based, Chi square method, and Singular Value Decomposition
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   • Assignment #2
7. Clustering: Kmeans, Hierarchical Agglomerative Clustering
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   • Video1 Video2
8. Ensemble Methods: Bagging and boosting
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9. Imbalanced Dataset: RUS, ROS, SMOTE
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10. Association Rules: Apriori Algorithm
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11. Recommendation Systems: Borda, Copeland
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12. Statistical Performances: Friedman test, Nemenyi, Bonferroni-Dunn
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Text books:

1. Oded Maimon, Lior Rokach, Data Mining and Knowledge Discovery Handbook, 2010
2. Jiawei Han, Micheline Kamber, Jian Pei, Data Mining Concepts and Techniques, 3rd Edition, 2011
3. Margaret H. Dunham, Data Mining – Introductory and  Advance Topics, Prentice Hall, 2003
4. Mohammed J. Zaki, Wagner Meira Jr., Data Mining and Analysis Fundamental Concepts and Algorithms, Cambridge, 2014