This article will discuss two clustering techniques, k-means and Hierarchical Clustering. Clustering is an unsupervised learning technique, with the goal bing to group to samples into a given number of partitions. Clustering uses uses the similarity between examples and groups examples based of their mutual similarities.

Wine such a multideminensional beverage. A feast for the senses, taste, smell even for the eyes. It also pairs well with statistical analysis techniques and even Lisp. What does wine and statistical analysis have to do with one another you may ask. Well the Wine dataset at it the 'goto' dataset used in just about every introduction cluster analysis. The Wine dataset is a chemical analysis of three types of wines wines grown in a region of Italy. The dataset contains an analysis of 178 samples, with 13 results of chemical assays for each sample. It is small enough yet contains enough complexity to be interesting.

While were on the subject of wine if you find yourself in eastern Washington, I highly recommend stopping by the Parisisos del Sol winery, good wine, down to earth atmosphere and a really interesting and knowledgeable proprietor.

But back to the cluster analysis...

Lets get started by loading the system necessary for this tutorial and creating a namespace to work in.