Clustering groups similar instances together from "summary" of Machine Learning by Ethem Alpaydin
Clustering is the process of grouping similar instances together based on some measure of similarity. The goal of clustering is to organize data into groups such that instances within the same group are more similar to each other than to those in other groups. This allows us to discover patterns and structures within the data that may not be immediately apparent. There are various clustering algorithms that can be used to achieve this goal. One common approach is the k-means algorithm, which partitions the data into k clusters by iteratively assigning instances to the cluster with the nearest mean. Another approach is hierarchical clustering, which builds a tree of clusters by successively merging the most similar clusters together. The choice of clustering algorithm and the number of clusters to use depend on the specific characteristics of the data and the goals of the analysis. For example, in some cases, we may have prior knowledge about the number of clusters that we expect to find, while in other cases, we may need to explore different options and evaluate the results based on some measure of cluster quality. Clustering is a valuable tool in machine learning and data analysis because it can help us to understand the underlying structure of the data and identify groups of similar instances. This can be useful for a wide range of applications, from customer segmentation to image processing. By grouping similar instances together, clustering allows us to simplify complex data and extract meaningful insights that can inform decision-making and drive innovation.
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