Unsupervised Learning - Kmeans Week 11 Challenge

Question No:-1 How does similarity is calculated if data is categorical in nature?

Ans:-Many data science techniques are based on measuring similarity and dissimilarity between objects. For example, K-Nearest-Neighbors uses similarity to classify new data objects. In Unsupervised Learning, K-Means is a clustering method which uses Euclidean distance to compute the distance between the cluster centroids and it’s assigned data points. Recommendation engines use neighborhood based collaborative filtering methods which identify an individual’s neighbor based on the similarity/dissimilarity to the other users.

Different from Euclidean distance is the Manhattan distance, also called ‘cityblock’, distance from one vector to another. You can imagine this metric as a way to compute the distance between two points when you are not able to go through buildings.

The Euclidean distance is a straight-line distance between two vectors.

Compared to the Cosine and Jaccard similarity, Euclidean distance is not used very often in the context of NLP applications. It is appropriate for continuous numerical variables. Euclidean distance is not scale invariant, therefore scaling the data prior to computing the distance is recommended. Additionally, Euclidean distance multiplies the effect of redundant information in the dataset. If I had five variables which are heavily correlated and we take all five variables as input, then we would weight this redundancy effect by five.