Week 6

This week’s topic is learning from observation where we learned about a technique used in unsupervised learning which is the clustering technique. Clustering techniques apply when there is no class to be predicted but rather when the instances are to be divided into natural groups. It is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples.

Clustering techniques are applied in various fields such as marketing, biology, libraries, city planning, insurance, and earthquake studies.

Distance measure will determine how the similarity of two elements is calculated and it will influence the shape of the clusters. There are two formulas to calculate the distance, euclidean and manhattan distance. There are two types of clustering algorithms, partitional and hierarchical algorithm.

We focused on K-means algorithm which is a partitional algorithm and we did exercises on using the K-means algorithm to cluster the data.