The paper introduces BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), an efficient data clustering method designed for very large databases. BIRCH incrementally and dynamically clusters multi-dimensional metric data points, aiming to produce high-quality clusters with limited resources (memory and time). It can typically achieve good clustering with a single scan of the data and further improve the quality with additional scans. BIRCH is also the first clustering algorithm to effectively handle "noise" (data points that are not part of the underlying pattern). The authors evaluate BIRCH's time/space efficiency, data input order sensitivity, and clustering quality through experiments. They compare BIRCH with CLARANS, another clustering method for large datasets, and show that BIRCH consistently outperforms it in terms of efficiency and clustering quality. BIRCH's architecture also offers opportunities for parallelism and interactive or dynamic performance tuning based on knowledge about the dataset gained during execution. The paper outlines the background, introduces the concepts of Clustering Feature (CF) and CF tree, and details the BIRCH algorithm. It discusses the process of inserting entries into a CF tree, the phases of the BIRCH clustering algorithm, and the handling of outliers and delay-split options. The authors present performance studies, including a complexity analysis and experiments using synthetic and real datasets, demonstrating BIRCH's scalability and robustness.The paper introduces BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), an efficient data clustering method designed for very large databases. BIRCH incrementally and dynamically clusters multi-dimensional metric data points, aiming to produce high-quality clusters with limited resources (memory and time). It can typically achieve good clustering with a single scan of the data and further improve the quality with additional scans. BIRCH is also the first clustering algorithm to effectively handle "noise" (data points that are not part of the underlying pattern). The authors evaluate BIRCH's time/space efficiency, data input order sensitivity, and clustering quality through experiments. They compare BIRCH with CLARANS, another clustering method for large datasets, and show that BIRCH consistently outperforms it in terms of efficiency and clustering quality. BIRCH's architecture also offers opportunities for parallelism and interactive or dynamic performance tuning based on knowledge about the dataset gained during execution. The paper outlines the background, introduces the concepts of Clustering Feature (CF) and CF tree, and details the BIRCH algorithm. It discusses the process of inserting entries into a CF tree, the phases of the BIRCH clustering algorithm, and the handling of outliers and delay-split options. The authors present performance studies, including a complexity analysis and experiments using synthetic and real datasets, demonstrating BIRCH's scalability and robustness.