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You're reading from Statistics for Machine Learning

Product typeBook

Published inJul 2017

Reading LevelIntermediate

PublisherPackt

ISBN-139781788295758

Edition1st Edition

Languages

Python

Concepts

Statistics

Author (1)

Pratap Dangeti

K-means clustering

Clustering is the task of grouping observations in such a way that members of the same cluster are more similar to each other and members of different clusters are very different from each other.

Clustering is commonly used to explore a dataset to either identify the underlying patterns in it or to create a group of characteristics. In the case of social networks, they can be clustered to identify communities and to suggest missing connections between people. Here are a few examples:

In anti-money laundering measures, suspicious activities and individuals can be identified using anomaly detection
In biology, clustering is used to find groups of genes with similar expression patterns
In marketing analytics, clustering is used to find segments of similar customers so that different marketing strategies can be applied to different customer segments accordingly

The k-means clustering algorithm is an iterative process of moving the centers of clusters or centroids to the mean position...

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Statistics for Machine Learning

Published in: Jul 2017Publisher: PacktISBN-13: 9781788295758

Author (1)

Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
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K-means clustering

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