01. 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 practice effective data-preprocessing techniques in lecture 1, laboratory classes 3 and assignment.

02. Evaluate major data mining classification methodologies.

Activities:

In lectures 3 to 6, student learn 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 under-fitting and over-fitting will also be discussed. Students will also apply various classification approaches to different datasets in laboratory classes 4 to 6 and assignments.

03. Critique association rules mining approaches.

Activities:

In lectures 7 and 8, students learn the concept of association analysis for transaction data, including frequent item sets, association rule mining, rule generation and evaluation, and Apriori algorithm. Students will practise association rules mining in laboratory classes 7 and 8 and assignment.

04. Evaluate Data Mining Algorithms based on data clustering techniques.

Activities:

In lectures 9 to 11 students learn major data clustering techniques, such as K-means clustering, hierarchical clustering, and DBSCAN, for pattern extraction and knowledge discovery from unlabeled data. Students will apply these approaches to real datasets in laboratory classes 9 and 10 and assignment.

05. Apply advanced data mining techniques for pattern discovery from selected datasets.

Activities:

In lecture the techniques will be demonstrated and students will apply the techniques in the laboratory class.