From the course: Machine Learning with Python: k-Means Clustering

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Why and when to use k-means clustering

Why and when to use k-means clustering - Python Tutorial

From the course: Machine Learning with Python: k-Means Clustering

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Why and when to use k-means clustering

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- [Instructor] In order to know when to use K-means clustering, we need to understand its strengths, and weaknesses. In terms of strengths, K-means clustering is based on simple statistical principles. It is a very flexible and malleable algorithm. This means that it can easily adapt to new examples. K-means clustering can be scaled to large data sets, and it can be applied to a wide set of real world situations, such as market segmentation, social network analysis, search result grouping, medical imaging and anomaly detection. There are some weaknesses inherent with K-means clustering as well. With K-means clustering, it isn't always clear what the appropriate value for K should be. There are several statistical measures, and approaches that we can leverage to make a more informed decision about the choice of K. However, we sometimes have to rely on domain knowledge to make this choice. The K means…

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