Traditional clustering techniques alone cannot resolve all challenges of partition-based clustering methods. In the partition based clustering, particularly in variants of K-means, initial cluster centre selection is a significant and crucial point. The dependency of final cluster is totally based on initial cluster centres; hence, this process is delineated to be most significant in the entire clustering operation. The random selection of initial cluster centres is unstable, since different cluster centre points are achieved during each run of the algorithm. Ensemble based clustering methods resolve challenges of partition-based methods. The clustering ensembles join several partitions generated by different clustering algorithms into a single clustering solution. The proposed ensemble methodology resolves initial centroid problems and improves the efficiency of cluster results. This method finds centroid selection through overall mean distance measure. The SimRank based similarity matrix find that the bipartite graph helps to ensemble.

中文翻译：

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基于SimRank的集成方法来解决分区聚类方法的挑战

单独的传统集群技术不能解决基于分区的集群方法的所有挑战。在基于分区的聚类中，尤其是在K均值的变体中，初始聚类中心的选择是重要而关键的一点。最终集群的依赖性完全基于初始集群中心。因此，该过程被描述为在整个聚类操作中最重要。初始聚类中心的随机选择是不稳定的，因为在算法的每次运行期间都会获得不同的聚类中心点。基于集合的聚类方法解决了基于分区的方法的难题。聚类集成将由不同聚类算法生成的多个分区合并为一个聚类解决方案。所提出的集成方法解决了初始质心问题并提高了聚类结果的效率。该方法通过整体平均距离测度找到质心选择。基于SimRank的相似度矩阵发现二分图有助于集成。