From the course: Becoming a Good Data Science Customer
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Clustering algorithms
From the course: Becoming a Good Data Science Customer
Clustering algorithms
- [Host] There is a wealth of different algorithms used for clustering, including Gaussian mix models, expectation maximization models, latent, Derishlay allocation methods, and fuzzy clustering algorithms. Besides the number of algorithms that exist, there are also many ways to measure performance, so you can't easily say one clustering solution is better than others. As a customer however, you don't need to try and learn the complete taxonomy of algorithms. Rather, it's useful to have a clear understanding of some of the most popular methods, and then ask the data science team members to explain which methods they used and why. What are the main types of clustering algorithms? One way to think about clustering algorithms is in terms of whether individuals are assigned to one group or whether they can belong to multiple clusters. Exclusive clusters are simple to understand. Customers belong to one and only one cluster. Other algorithms allow customers to be shared across multiple…
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