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Cluster analysis

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What is Clustering? Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters. Clustering is an unsupervised learning algorithm, but it can also be used to improve the accuracy of supervised machine learning algorithms by clustering the data points into similar groups and using these cluster labels as independent variables in the supervised machine learning algorithm. Let’s understand this with an example. Suppose the head of a rental store wish to understand preferences of the customers to scale up the business. Ideally it is not possible to look at details of each costumer and devise a unique business strategy for each one of them. But, what can be done is to cluster all the costumers into say 10 groups based on their purchasing habits and use a separate strategy for costumers in each of these 10 groups. And this is what we call clustering. Measures of Similarity and Dissimilarity Distance or similarity measures are essential in solving many pattern recognition problems such as classification and clustering. Various distance/similarity measures are available in the literature to compare two data distributions. As the names suggest, a similarity measures how close two distributions are. Similarity Measure Numerical measur ...
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