An overview of clustering algorithms, including centroid-based (K-Means, K-Means++), density-based (DBSCAN), hierarchical, and distribution-based clustering. The article explains how each type works, its pros and cons, provides code examples, and discusses use cases.
tokenizing and stemming each synopsis transforming the corpus into vector space using tf-idf calculating cosine distance between each document as a measure of similarity clustering the documents using the k-means algorithm using multidimensional scaling to reduce dimensionality within the corpus plotting the clustering output using matplotlib and mpld3 conducting a hierarchical clustering on the corpus using Ward clustering plotting a Ward dendrogram topic modeling using Latent Dirichlet Allocation (LDA)
