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Clustering Rooted Ordered Trees

Authors: Mostafa Haghir Chehreghani; Masoud Rahgozar; Caro Lucas; Morteza Haghir Chehreghani;

Clustering Rooted Ordered Trees

Abstract

Tree structures have gained popularity for storing data from different domains such as XML documents, bio informatics and so on. Clustering these data can facilitate different operations. In this paper, we propose TreeCluster, a novel and heuristic algorithm for clustering tree structured data. This algorithm considers a representative tree for each cluster. For each input tree T, TreeCluster computes the composition of the tree T and each of the clusters. Tree T belongs to the cluster which its composed tree gains the best score. After adding a tree to a cluster the representative tree of that cluster is updated. We evaluate the accuracy of the TreeCluster algorithm in comparison to the previous works

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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