Hierarchical clustering is the second most popular technique for clustering after K-means. Remember, in K-means; we need to define the number of clusters beforehand. However, in hierarchical clustering, we don’t have to specify the number of […]

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## Introduction to K-means

K-means clustering is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. Before we venture into K-means, let’s first understand what clustering is? What is clustering? The idea behind clustering is […]

Continue readingMore Tag## Introduction to Principal Component Analysis(PCA)

Principal Component Analysis is an unsupervised dimensionality reduction technique, which is extensively used in machine learning. It helps us to alleviate the problem of the curse of dimensionality by reducing the dimension of the data. PCA […]

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