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<p><b>Abstract</b>—In this paper, we introduce the <em>ClusterTree</em>, a new indexing approach to representing clusters generated by any existing clustering approach. A cluster is decomposed into several subclusters and represented as the union of the subclusters. The subclusters can be further decomposed, which isolates the most related groups within the clusters. A <em>ClusterTree</em> is a hierarchy of clusters and subclusters which incorporates the cluster representation into the index structure to achieve effective and efficient retrieval. Our cluster representation is highly adaptive to any kind of cluster. It is well accepted that most existing indexing techniques degrade rapidly as the dimensions increase. The <em>ClusterTree</em> provides a practical solution to index clustered data sets and supports the retrieval of the nearest-neighbors effectively without having to linearly scan the high-dimensional data set. We also discuss an approach to dynamically reconstruct the <em>ClusterTree</em> when new data is added. We present the detailed analysis of this approach and justify it extensively with experiments.</p>
Indexing, cluster representation, nearest-neighbor search, high-dimensional data sets.

A. Zhang and D. Yu, "<em>ClusterTree</em>: Integration of Cluster Representation and Nearest-Neighbor Search for Large Data Sets with High Dimensions," in IEEE Transactions on Knowledge & Data Engineering, vol. 15, no. , pp. 1316-1337, 2003.
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