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Clustering algorithms group similar data points together from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Clustering algorithms are a powerful tool in the data scientist's arsenal for uncovering patterns in data. These algorithms are designed to group together data points that share similar characteristics or properties. By doing so, they allow us to identify natural groupings within our data that may not be readily apparent to the naked eye. The process of clustering involves partitioning a set of data points into clusters, with the goal of maximizing the similarity within each cluster while minimizing the similarity between clusters. This is achieved by defining a distance metric that quantifies the similarity between data points, and then using this metric to iteratively assign data points to clusters based on their similarity to one another. One of the key advantages of clustering algorithms is that they do not require labeled data, meaning that they can be used to uncover patterns in unlabeled datasets where the true grouping of data points is unknown. This makes clustering algorithms particularly useful in exploratory data analysis, where we may be trying to gain insights into the structure of our data without any prior knowledge of how it is organized. There are many different clustering algorithms available, each with its own strengths and weaknesses. Some algorithms, such as K-means clustering, partition the data into a predetermined number of clusters based on the mean of the data points in each cluster. Others, like hierarchical clustering, build a tree-like structure of clusters by iteratively merging or splitting clusters based on their similarity.- The choice of clustering algorithm will depend on the nature of the data and the goals of the analysis. However, the overarching goal remains the same: to group similar data points together in order to uncover hidden patterns and structures within the data.
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