Data Mining Virtual Labs

Introduction

The Data Mining virtual lab provides a platform for students to understand the concepts of Data mining and their application.

Target Audience

This lab is useful to undergraduate or postgraduate students who are starting to learn about the field of Data mining. Prior knowledge about databases and data structures would be helpful, although not mandatory. Data mining is a popular elective in most undergraduate / postgraduate CS curricula across the World, and is typically taken in the 3rd/4th year of undergraduate study.

List of Experiments

The list of experiments is as follows:

1. Apriori (An association rule mining algorithm)
2. Sampling (An association rule mining algorithm)
3. Partition (An association rule mining algorithm)
4. Comparison of Association Rule Mining Algorithms
5. k-Means (A partitioning clustering algorithm)
6. Agglomerative Clustering (A hierachial clustering algorithm)
7. DBSCAN (A density-based clustering algorithm)
8. kNN (A nearest neighbour based classification algorithm)
9. Decision Tree (Classification algorithm)
10. Comparison of Classification algorithms

Note that to fully experience the interactive content in these labs, you must first login. To login, please click here. You will be redirected to a login page and after successful authentication a list of experiments will be displayed. Then, please click on the page titled: Data Mining Virtual Labs. You will be redirected back to this page and can then click on any experiment above to see their interactive content. For more details on how to use the interactive content, please see the procedure page.

Courses Aligned

• Data Mining
• Data Ware housing
• Web Data and Knowledge Management

Pre-requisite Softwares

• A java script enabled web-browser

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