01. Explain the technologies and applications of data mining techniques (DM).

Activities:

Lecture 1 is on the introduction of data mining techniques and its applications.

02. Identify various data types and perform critical and effective data-preprocessing tasks

Activities:

Students will learn different types of data and their related issues such as sampling, similarity metrics, feature selection, dimensionality issue. They also learn and practise effective data-preprocessing techniques in Lecture 2, Tutorial 3, and assignment.

03. Explain and evaluate major classification methods

Activities:

Lectures 3, 4, 5, and 6 provide details for a wide range of classification approaches such as decision tree, rule-based classification, nearest neighbour classification, Bayes classification, artificial neural network (ANN), and support vector machine (SVM). Related issues covering underfitting and overfitting will also be discussed. Students will also apply various classification approaches to different datasets in Tutorials 4, 5, 6, and assignment.

04. Explain and evaluate association rules mining approaches

Activities:

Lectures 7 and 8 introduce association analysis for transaction data, including frequent itemsets, association rule mining, rule generation and evaluation, and Apriori algorithm. Students will practise association rules mining in Tutorials 7, 8, and assignment.

05. Explain and evaluate data clustering techniques

Activities:

Lectures 9, 10, and 11 introduce major data clustering techniques, such as K-means clustering, hierarchical clustering, and DBSCAN, for pattern extraction and knowledge discovery from unlabeled data. Students will learn how to applies these approaches to real datasets in Tutorials 9, 10, and assignment.

06. Discuss advanced issues of data mining algorithms, e.g. classification systems, important issues in clustering algorithms, and the links between data mining and knowledge engineering.

Activities:

Lectures 12 is a revision lecture where all relevant data mining algorithms will be evaluated for deeper understandings and applications. Also, students will analyse advanced issues such as classifications systems, and the links between data mining and knowledge engineering, for their further studies.

07. Implement advanced data mining techniques for pattern discovery from selected datasets.

Activities:

Students will learn the basics of MATLAB programming and WEKA in Tutorials including data pre-processing, decision tree, ANN, SVM,and association rules mining. Students will apply k-means and hierarchical approach for MATLAB data clustering. Assignment consultations will be given in certain tutorials, where students will get help and technical support to overcome their difficulties encountered in doing their assessments.