Micro-Clustering：在大多样性中发现小集群
Micro-Clustering: Finding Small Clusters in Large Diversity
摘要：
我们解决的问题，无监督的软聚类称为微聚类。这个问题的目的是枚举所有的组组成的记录强烈相关，而标准的聚类方法分离记录稀疏的部分。微聚类的问题公式化是不平凡的。相似图中的团挖掘是一种典型的方法，但它会导致大量的团，这些团是由许多相似的团组成的。提出了数据抛光的新概念。巨大的解决方案的原因可以被认为是数据中的组不清楚，即组的边界不清楚，因为噪音，不确定性，谎言，缺乏等。具体来说，对应于聚类的稠密子图被团取代。聚类被明确为最大团，因此最大团的数量将大大减少。我们还提出了一个有效的算法，即使适用于大规模的数据。计算实验表明了该算法的有效性，解决方案的数量很小，（例如，1，000个），每个组的成员都有很深的关联，计算时间短。
英文摘要：
We address the problem of un-supervised soft-clustering called micro-clustering. The aim of the problem is to enumerate all groups composed of records strongly related to each other, while standard clustering methods separate records at sparse parts. The problem formulation of micro-clustering is non-trivial. Clique mining in a similarity graph is a typical approach, but it results in a huge number of cliques that are of many similar cliques. We propose a new concept data polishing. The cause of huge solutions can be considered that the groups are not clear in the data, that is, the boundaries of the groups are not clear, because of noise, uncertainty, lie, lack, etc. Data polishing clarifies the groups by perturbating the data. Specifically, dense subgraphs that would correspond to clusters are replaced by cliques. The clusters are clarified as maximal cliques, thus the number of maximal cliques will be drastically reduced. We also propose an efficient algorithm applicable even for large scale data. Computational experiments showed the efficiency of our algorithm, i.e., the number of solutions is small, (e.g., 1,000), the members of each group are deeply related, and the computation time is short.
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