How to Take Advantage of the Limitations with Markov Clustering?–The Foundations of Branching Markov Clustering (BMCL)
How to Take Advantage of the Limitations with Markov Clustering?–The Foundations of Branching Markov Clustering (BMCL)
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发表时间：
2008
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通讯作者：
Hiroyuki Akama;Maki Miyake;Jaeyoung Jung
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作者：
Hiroyuki Akama;Maki Miyake;Jaeyoung Jung
在本文中，我们提出了一种新的方法，以最佳地采用MCL（马尔可夫聚类算法）的“中和”的微不足道的缺点承认其原来的提议者。我们的BMCL（分支马尔可夫聚类）算法可以将一个大的核心集群细分为适当大小的子图。利用三个语料库，我们研究的BMCL的影响，根据网络中的枢纽的曲率（聚类系数）而变化。
In this paper, we propose a novel approach to optimally employing the MCL (Markov Cluster Algorithm) by “neutralizing” the trivial disadvantages acknowledged by its original proposer. Our BMCL (Branching Markov Clustering) algorithm makes it possible to subdivide a large core cluster into appropriately resized sub-graphs. Utilizing three corpora, we examine the effects of the BMCL which varies according to the curvature (clustering coefficient) of a hub in a network.