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Clustering algorithms group data points based on similarities from "summary" of Machine Learning by Stephen Marsland
Clustering algorithms are used in machine learning to group data points that are similar to each other. These algorithms aim to find patterns in the data that can help organize it into meaningful groups. By identifying similarities between data points, clustering algorithms help to uncover underlying structures within the data. One common type of clustering algorithm is the k-means algorithm, which aims to partition data points into k clusters based on their distance from the mean of each cluster. The algorithm iteratively assigns data points to clusters and updates the cluster centroids until the algorithm converges. By grouping data points based on their proximity to each other, the k-means algorithm can help identify distinct clusters in the data. Another popular clustering algorithm is hierarchical clustering, which builds a tree of clusters by merging or splitting clusters based on their similarities. This algorithm does not require the number of clusters to be predefined, allowing it to uncover the natural clustering structure of the data. Hierarchical clustering is useful for visualizing the relationships between data points and identifying clusters at different levels of granularity. Density-based clustering algorithms, such as DBSCAN, group data points based on their density within the dataset. These algorithms can identify clusters of varying shapes and sizes, making them useful for detecting outliers and noise in the data. By focusing on the density of data points, density-based clustering algorithms can uncover clusters that may not be apparent with distance-based algorithms.- Clustering algorithms play a crucial role in unsupervised learning by organizing data into meaningful groups based on similarities. These algorithms help to uncover patterns and structures in the data that can provide valuable insights for further analysis and decision-making. By grouping data points based on similarities, clustering algorithms enable the exploration and understanding of complex datasets in a systematic and efficient manner.
