
84 Knowledge Discovery from Data Streams
Figure 6.1: The Clustering Feature Tree in BIRCH. B is the maximum number
of CFs in a level of the tree.
the CF-tree, where each node is a tuple (Clustering Feature) that contains
the sufficient statistics describing a set of data points, and compresses all in-
formation of the CFs below in the tree. BIRCH only works with continuous
attributes. It was designed for very large datasets, explicitly taking into ac-
count time and memory constraints. For example, not all data points are used
for clustering, dense regions of data points are treated as sub-clusters. BIRCH
might scan data twice to refine the CF-tree, although ...