Machine Learning Clustering Algorithm Implementation

Please read the instructions carefully. Implement the following algorithms from scratch. You may use any programming language that you like. - Single linkage - Complete linkage - Average linkage - Lloyd’s method Test it on 2 to 3 data sets. You may use any data sets. For example. The UCI datasets page includes any excellent ones: [login to view URL] Please choose the data carefully, so that it is appropriate for clustering. (Ex. the Iris data set). In addition, it is highly advised to test your algorithm on some of the small examples like the ones we did in class, in order to make sure that the implementation is correct. Compare the output of your algorithm against the “target truth” in the data sets you use, by using Hamming distance. Summarize your findings in a table, and specify which algorithm did best for each data set. The above can earn you a grade of up to a B. To reach up to a grade of A+, you need to complete one of the following: - K-means++ ([login to view URL]) - BDSCAN clustering algorithm - Any other clustering algorithm - Compare clusterings using misclassification error (in addition to Hamming distance): [login to view URL] (Section 5)