Clustering is a type of Unsupervised Learning in data science that involves grouping a set of objects in a way that objects in the same group (cluster) are more similar to each other than to objects in other clusters. Clustering algorithms do not rely on a pre-existing set of labeled data, but instead use some measure of similarity or distance between objects to identify clusters.
Clustering is commonly used in a wide range of applications, including market segmentation, image segmentation, text clustering, anomaly detection, and gene expression analysis, among others. Some commonly used clustering algorithms in data science include K-Means clustering, hierarchical clustering, and density-based clustering.
The choice of clustering algorithm and the evaluation of the resulting clusters depend on the specific problem and the characteristics of the data. Commonly used evaluation metrics for clustering include Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index, which measure the compactness, separation, and overall quality of the clusters.
Clustering can also be combined with other techniques such as Dimensionality Reduction and Feature Extraction to improve the quality and interpretability of the clusters. Clustering is a powerful technique in exploratory data analysis as it helps to identify hidden structures in the data, which can then be used for further analysis and decision-making.
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