Introduction to Clustering

It is basically a type of unsupervised learning method. Which means the type of algorithm that learns patterns from untagged data. An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labeled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples.

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 and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them.  

So we have to collect data from different sources like schools, colleges, apps, construction, e . t .c.,. From this collected data we have execute the program on machine learning on python. The clustering project is done by me by using google Coolab.

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Why clustering is used?

The clustering is  belonging naturally the grouping among the data we have collected. There are no criteria for good clustering. It depends on the user, who are using clustering based projects.

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Clustering Algorithms:

k-means clustering: It is a simplest unsupervised learning algorithm that slove the problems on clustering. K-means the partitions n observations into k clusters with the nearest mean a quantity is suitable for a served to a person as a prototype of the cluster.

Applications of Clustering in different fields  

• Marketing: It can be used to characterize & discover customer segments for marketing purposes.
• Biology: It can be used for classification among different species of plants and animals.
• Libraries: It is used in clustering different books on the basis of topics and information.

Insurance: It is used to acknowledge the customers, their policies and identifying the frauds.

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Written by J. Sreeemai, a Siliconvalley4u's student