Cluster Analysis groups similar data with respect to properties or attributes. It’s very instrumental for a plethora of fields and applications from Segmentation to Market Analysis, Recommender Systems to Medical Imaging, Anomaly Detection to Geospatial Data and a lot more. It doesn’t matter if the number of groups is defined by algorithms or decided beforehand, Cluster Analysis insightfully & critically suggests the data that should be clubbed.
Everything you require to begin with cluster analysis will be given to you in this course. You will learn the most common clustering techniques after gaining a basic understanding of cluster analysis is and its real-life applications.
k-means Clustering
Density-based Spatial Clustering of Applications with Noise (DBSCAN)
Hierarchical Agglomerative Clustering (HAC)
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