stream (version 1.3-0)

DSC_BIRCH: Balanced Iterative Reducing Clustering using Hierarchies

Description

BIRCH builds a balanced-tree of Clustering Features (CFs) to summarize the stream. A CF is a tuple (n, LS, SS) which represents a cluster by storing the number of elements (n), their linear sum (LS) and their squared sum (SS). Each new observation descends the tree by following its closest CF until a leaf node is reached. It is either merged into its closest leaf-CF or inserted as a new one. All leaf-CFs form the micro-clusters. Rebuilding the tree is realized by inserting all leaf-CF nodes into a new tree structure with an increased treshold.

Usage

DSC_BIRCH(treshold, branching, maxLeaf, maxMem = 0, outlierThreshold = 0.25)

Arguments

treshold

treshold used to check whether a new datapoint can be absorbed or not

branching

branching factor (maximum amount of child nodes for a nonleaf node) of the CF-Tree.

maxLeaf

maximum number of entries within a leaf node

outlierThreshold

threshold for identifying outliers when rebuilding the CF-Tree

maxMem

memory limitation for the whole CFTree in bytes. Default is 0, indicating no memory restriction.

References

Zhang T, Ramakrishnan R and Livny M (1996), "BIRCH: An Efficient Data Clustering Method for Very Large Databases", In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data. Montreal, Quebec, Canada , pp. 103-114. ACM.

Zhang T, Ramakrishnan R and Livny M (1997), "BIRCH: A new data clustering algorithm and its applications", Data Mining and Knowledge Discovery. Vol. 1(2), pp. 141-182.

Examples

# NOT RUN {
stream <- DSD_Gaussians(k = 3, d = 2)

BIRCH <- DSC_BIRCH(treshold = .1, branching = 8, maxLeaf = 20)
update(BIRCH, stream, n = 500)

plot(BIRCH,stream)

# }