Efficient Data Summarization Using Contemporary Clustering Techniques

In order to summarize complex datasets for exploratory data analysis, this study investigates the efficacy of clustering algorithms such Spectral Clustering, Mean-shift Clustering, KMeans Clustering, DBSCAN Clustering, and Agglomerative hierarchical Clustering. Strong performance from clusters KMeans, Spectral, and Mean-shift is revealed by evaluating metrics such silhouette scores, Davies-Bouldin scores, and Calinski-Harabasz scores. The ensemble clusters K-Means, DBSCAN, and Agglomerative Heirarchial Clustering exhibit the best overall performance. Case studies from a variety of fields, including anomaly detection and document clustering, emphasize the practical uses of clustering by showcasing its capacity to produce informative data summaries.