Hierarchical Clustering - Fun and Easy Machine Learning with Examples Hierarchical Clustering Looking at the formal definition of Hierarchical clustering, as the name suggests is an algorithm that builds hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left. The results of hierarchical clustering can be shown using Dendogram as we seen before which can be thought of as binary tree Difference between K Means and Hierarchical clustering Hierarchical clustering can't handle big data well but K Means clustering can.

In pattern recognition, the KNN algorithm is a method for classifying objects based on closest training examples in the feature space. KNN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is delayed until classification. The KNN is the fundamental and simplest classification technique when there is little or no prior knowledge about the distribution of the data. The K in KNN refers to number of nearest neighbors that the classifier will use to make its predication. In this video we use Game of Thrones example to explain kNN.