Birch

Abstract

Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification of clusters, or densely populated regions, in a multi-dimensional dataset. Prior work does not adequately address the problem of large datasets and minimization of I/O costs.This paper presents a data clustering method named BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and dynamically clusters incoming multi-dimensional metric data points to try to produce the best quality clustering with the available resources (i.e., available memory and time constraints). BIRCH can typically find a good clustering with a single scan of the data, and improve the quality further with a few additional scans. BIRCH is also the first clustering algorithm proposed in the database area to handle "noise" (data points that are not part of the underlying pattern) effectively.We evaluate BIRCH 's time/space efficiency, data input order sensitivity, and clustering quality through several experiments. We also present a performance comparisons of BIRCH versus CLARANS, a clustering method proposed recently for large datasets, and show that BIRCH is consistently superior.

Benefit Segmentation of Outbound Summer Package Tourists

“…In order to reveal the grouping of tourists according to their preferred benefits, a twostep cluster analysis has been applied using the log-likelihood measure (Chiu et al, 2001;Zhang et al, 1996). The number of clusters have been determined using Schwarz's Bayesian Criterion (BIC).…”

Automated Detection and Analysis of Particle Beams in Laser-Plasman Accelerator Simulations

“…We present an algorithm to sample the simulation data, but Monte Carlo methods [Banfield & Raftery (1993)] could be used by adding a repetitive randomness process as a way of guaranteeing representation of a beam candidate region and improvement of accuracy. Future evaluations may consider more sophisticated methods such as Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) as in Zhang et al (1996) and hierarchical clustering based on granularity as in Liang & Li (2007), which are designed for very large data sets. Further investigation should also include subspace clustering as in Kriegel et al (2009) once the large simulation datasets contain target regions that can be determined using the techniques proposed in our framework.…”

A Statistical Framework to Evaluate Extreme Weather Definitions from a Health Perspective: A Demonstration Based on Extreme Heat Events

“…We applied a hierarchical clustering technique and employed an average distance metric to determine distances between clusters that might be merged in each step of the clustering process (Kalkstein et al 1987; Zhang et al 1996). Average distance is calculated using the formula…”