BIRCH is an efficient data clustering method for very large databases. It incrementally and dynamically clusters incoming multi-dimensional data points to produce high-quality clusters with limited resources. BIRCH can find a good clustering with a single scan and improve quality with additional scans. It effectively handles noise and is the first clustering algorithm in databases to address outliers. Experiments show BIRCH outperforms CLARANS in time/space efficiency, data input order sensitivity, and clustering quality. BIRCH's architecture supports parallelism and interactive tuning. It uses a CF tree to summarize data, allowing efficient clustering with minimal I/O. BIRCH is suitable for large datasets and can handle skewed data. It is efficient, scalable, and robust to input order. BIRCH's performance is linear in dataset size and is less sensitive to input order. It can trade off memory and time for similar final quality. BIRCH is compared with CLARANS, which requires more memory and has worse performance. BIRCH is faster and produces better quality clusters. BIRCH is suitable for large datasets and can handle skewed data. It is efficient, scalable, and robust to input order. BIRCH is the best available clustering method for very large databases.BIRCH is an efficient data clustering method for very large databases. It incrementally and dynamically clusters incoming multi-dimensional data points to produce high-quality clusters with limited resources. BIRCH can find a good clustering with a single scan and improve quality with additional scans. It effectively handles noise and is the first clustering algorithm in databases to address outliers. Experiments show BIRCH outperforms CLARANS in time/space efficiency, data input order sensitivity, and clustering quality. BIRCH's architecture supports parallelism and interactive tuning. It uses a CF tree to summarize data, allowing efficient clustering with minimal I/O. BIRCH is suitable for large datasets and can handle skewed data. It is efficient, scalable, and robust to input order. BIRCH's performance is linear in dataset size and is less sensitive to input order. It can trade off memory and time for similar final quality. BIRCH is compared with CLARANS, which requires more memory and has worse performance. BIRCH is faster and produces better quality clusters. BIRCH is suitable for large datasets and can handle skewed data. It is efficient, scalable, and robust to input order. BIRCH is the best available clustering method for very large databases.