Data Mining Guidelines and Practical List
Course Objective: This course introduces data mining techniques and enables students to apply these techniques on real-life datasets. The course focuses on three main data mining techniques: Classification, Clustering and Association Rule Mining tasks.
Data Mining Guidelines
Introduction to Data Mining – Applications of data mining, data mining tasks, motivation and challenges, types of data attributes and measurements, data quality.
Data Pre-processing – aggregation, sampling, dimensionality reduction, Feature Subset Selection, Feature Creation, Discretization and Binarization, Variable Transformation.
Classification: Basic Concepts, Decision Tree Classifier: Decision tree algorithm, attribute selection measures, Nearest Neighbour Classifier, Bayes Theorem and Naive Bayes Classifier,
Model Evaluation: Holdout Method, Random Sub Sampling, Cross-Validation, evaluation metrics, confusion matrix.
Association rule mining: Transaction data-set, Frequent Itemset, Support measure, Apriori Principle, Apriori Algorithm, Computational Complexity, Rule Generation, Confidence of association rule.
Cluster Analysis: Basic Concepts, Different Types of Clustering Methods, Different Types of Clusters, K-means: The Basic K-means Algorithm, Strengths and Weaknesses of K-means algorithm, Agglomerative Hierarchical Clustering: Basic Algorithm, Proximity between clusters,
DBSCAN: The DBSCAN Algorithm, Strengths and Weaknesses.
Data Mining Reference Books:
- Han, J., Kamber, M.,& Jian,P. (2011). Data Mining: Concepts and Techniques. 3rd edition. Morgan Kaufmann
- Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining. 1st Edition. Pearson Education