PCA, Singular Value Decomposition & Association Rules implementation in Python

I am experiment with Principal Component Analysis as a dimensionality reduction approach to assist in clustering high-dimensional data. For this I have use an image segmentation data set for clustering. After clustering the image data, I measure completeness and homogeneity of the generated clusters. The data set used in this problem is based on the Image Segmentation data set at the UCI Machine Learning Repository..

In addition I would perform association rule mining using the implementation provided in the textbook Machine Learning in Action, Chapter 11. For this implementation I use a modified version of the Apriori implementation in Machine Learning in Action. It has been modified to compute lift values for rules in addition to confidence

The final experimentation is with item-based recommendation for a joke recommender system using a modified version of the item-based recommender algorithm from Ch. 14 of the textbook mention above. I will use it on joke ratings data based on Jester Online Joke Recommender System .

See implementation