Data Clustering: Techniques, Applications, and Future Directions

Clustering is an important tool in artificial intelligence, machine learning, and data mining, which is used for pattern recognition, decision making, and exploratory data analysis. In this paper we provide a comprehensive analysis to using five of the most popular clustering algorithms – K-Means, DBSCAN, Agglomerative Hierarchical Clustering, GMM, and FCM. The concept of each algorithm, its advantages and area of applications are discussed in this review. The paper discusses their performance on AI based automation, big data analytics, anomaly detection and text mining. Cluster evaluation criteria along with internal and external validation methods are considered for evaluating clustering performance. And in addition, we will shine a spotlight on the real-world applications of these clustering techniques and the situations which are most appropriate to use each one of them when working with AI, ML, as well as data mining. We conclude the paper by discussing the critical challenges associated with high-dimensional data, optimal model building, and model interpretability, and provide insights on the future directions of research in clustering