CAJ | 학술논문

异构信息网络聚类分析是当前的热点研究问题之一。利用异构信息网络的稀疏性，该文提出一种基于嵌入技术的星型模式的异构信息网络的快速聚类算法。首先从相容的角度将异构信息网络转化为若干个相容的二部图，使用随机映射和一种线性时间求解程序快速计算出每个二部图的近似通勤距离嵌入，每个嵌入都存在一个子集指示目标数据集；然后，使用这些指示子集构建一个通用的聚类模型；最后，将所有指示子集的类设置标号，通过计算指示同一目标对象的指示数据与标号相同类的中心点的加权距离总和，同时划分所有的指示子集，从而快速获得通用模型的极小值。通过理论分析及实验验证，该文算法聚类速度快，聚类准确率高。
이구신식망락취류분석시당전적열점연구문제지일。이용이구신식망락적희소성，해문제출일충기우감입기술적성형모식적이구신식망락적쾌속취류산법。수선종상용적각도장이구신식망락전화위약간개상용적이부도，사용수궤영사화일충선성시간구해정서쾌속계산출매개이부도적근사통근거리감입，매개감입도존재일개자집지시목표수거집；연후，사용저사지시자집구건일개통용적취류모형；최후，장소유지시자집적류설치표호，통과계산지시동일목표대상적지시수거여표호상동류적중심점적가권거리총화，동시화분소유적지시자집，종이쾌속획득통용모형적겁소치。통과이론분석급실험험증，해문산법취류속도쾌，취류준학솔고。
Research on clustering heterogeneous information networks is one of the current hotspots. Taking advantages of the sparsity of heterogeneous information networks, a fast clustering algorithm based on embedding technology for heterogeneous information networks of star network schema is proposed in this paper. First, the heterogeneous information network is transformed into some compatible bipartite graphs from the point of compatible view. Then, the approximate commute distance embedding of each bipartite graph is computed via random mapping and a linear time solver, and an indicator subset in each embedding indicates the target dataset. At last, a general model is formulated via all the indicator subsets, and a minimum value of the model is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. This proposed algorithm is effective by theory analysis and experimental verification.