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An evaluation against the BIRCH [ZRL96] and the stream algorithm from [OMM+ 02] showed both faster execution times and higher cluster accuracy for the CluStream algorithm. Zhong [Zho05] uses the ideas behind Aggarwal’s framework in order to cluster text streams with a stream variant of the spherical k-means algorithm and combines it with principles from machine learning. The algorithm does not only assign new documents to clusters using their cosine similarity (Eq. 12)) to the center 44 CHAPTER 2 of the respective clusters.

The online component summarizes the cluster structure in so-called micro clusters that contain a condensed representation of the clusters at given times; the granularity of these clusters decreases with the age of the clusters. The offline component builds upon these micro-clusters to offer different time frames for the analysis of the data stream by combining clusters inside the time frame requested by the user. Since it can operate on the summaries provided by the online component, the authors claim that it can execute its computations efficiently.

In the latter case, the entry is composed of at most B children containing the subclusters where B is a predefined value, the so-called branching factor. When inserting a new web document, it is passed down the tree starting from the root node. In each step, the child it has the highest similarity to is selected as long as the similarity is above a given threshold. When no such child exists at a given step, the element is inserted as a new child in the current node. If in one of these cases the node created by this procedure has more than B children, it is split by choosing the pair of child nodes with the lowest pairwise similarity and using them as seeds for the newly created clusters.

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