MASTER OF COMPUTER and INFORMATION SCIENCES COMP809 Data Mining & Machine Learning Semester 1, 2020
You have been employed as a data scientist by a large data analytics company and your first project has gone well. Your first project involved supervised learning and you were able to apply the methods that you covered in Assessment 1 of this course.
However, the second project that you have been assigned to involves datasets where labels are not relevant to the problem at hand or they involve too much time commitment from domain specialists to label them. You realise the only method of solution is to apply unsupervised learning, specifically clustering.
As you have been assigned datasets from four very different application environments you have decided that the best approach is to explore three widely used clustering algorithms and deploy each of them on the different datasets.
The three algorithms that you have decided to explore are 1) K Means 2) DBSCAN and 3) Agglomerative.
The four datasets that you have been given are: 1) Dow Jones Indexhttps://archive.ics.uci.edu/ml/datasets/Dow+Jones+Index# 2) Facebook Live Sellers in Thailand https://archive.ics.uci.edu/ml/datasets/Facebook+Live+Sellers+in+Thailand 3) Sales Transactions https://archive.ics.uci.edu/ml/datasets/Sales_Transactions_Dataset_Weekly 4) Water Treatment Plant https://archive.ics.uci.edu/ml/datasets/Water+Treatment+Plant
You will need to complete three tasks as detailed below.
For each activity in this task you must apply a suitable feature selection algorithm before deploying each clustering algorithm. Your clustering results should include the following measures:
Time taken, Sum of Squares Errors (SSE), Cluster Silhouette Measure (CSM)
Submit Python code used for parts a) to c) below. You only need to submit the code for one of the 4 datasets.
In the event that no single algorithm performs best on all three performance measures you will need to carefully consider how you will rate each of the measures and then decide how you will produce an overall measure that will enable you to rank the algorithms. (12 marks)
This task requires you to do some further research on your own. The t-sne algorithm (https://lvdmaaten.github.io/tsne/) was designed to visualize high dimensional data after reducing dimensionality.
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