Mining the outliers that are different from normal data objects in the network is one of the important tasks in data mining. At present

the research aiming at outlier detection in bi-typed heterogeneous information network is relatively small. The methods which are applicable to homogeneous network can not be applied to bi-typed heterogeneous networks. Therefore

we propose a Rank-Kmeans Based Outlier detection method

called RKBOutlier

in heterogeneous information network. The two kinds of the objects and the connected semantic information are extracted from the heterogeneous information network. One type of the objects is regarded as the attribute objects

another type of the objects is regarded as the target objects. We perform cluster partitioning on target objects to detect the distribution of the attribute objects in each cluster. The objects which are abnormal at data distribution are considered to be the outliers. Ranking and clustering are combined to significantly improve the accuracy of clustering. The experimental results show that RKBOutlier can effectively detect outliers in bi-typed heterogeneous information network.