基于电阻网络的异构数据协同聚类算法

传统聚类方法处理的是同构数据，无法满足异构数据同时聚类的应用需求，聚类结果的准确率较低，标签可读性较差。针对上述问题，提出一种基于电阻网络的异构数据协同聚类算法。该算法将异构关联数据抽象为多部图形式的电阻网络，进行特征计算及聚类。在对异构数据进行协同聚类后，可以得到一种聚类结构，其中每一类包含多种异构数据，它们之间可以互为标签，标签可读性高。实验结果证明，该方法是一种切实可行且效果优异的数据聚类算法。

Co-clustering Algorithm for Heterogeneous Data Based on Resistive Network

As traditional cluster methods focusing on the homogeneous data can not meet the need of simultaneous clustering of heterogeneous data,the precious is low,and the readability of the labels is poor,this paper presents a co-clustering algorithm for heterogeneous data based on resistive network.In the algorithm,the heterogeneous related data is transformed into a resistive network with multi-part graph structure for the following computing of eigenvalue and clustering.After co-clustering,a clustering result structure can be obtained,that in the structure one class includes multiple heterogeneous data which can be each other＇s label,and the readability of the labels is high.Experimental results prove that the data clustering algorithm is achievable and effective.