Abstract: One-class-classifier （OCC） aims to distinguish a target class from outliers. Existing OCC algorithms based on hyperplane, such as one-class SVM （OCSVM） and Mahalanobis one-class SVM （MOCSVM）, solve this problem by finding a hyperplane with the maximum distance to the origin. However, since they either neglect the structure of the given data or just takes the structure into account in a relatively coarse granularity, only the suboptimal hyperplane may be abtained. In order to mitigate this problem, a novel OCC named enhanced one-class SVM （EnOCSVM） is proposed. First obtaining the distribution of the target data by the unsupervised methods such as agglomerative hierarchical clustering, and then embedding the cluster information into the original OCSVM framework, EnOCSVM can optimize the tightness of target data and maximizes the margin from the origin simultaneously. In this way, EnOCSVM not only takes much more priori knowledge into account than the above algorithms, but also provides a general method to extend the present SVM algorithm to consider intrinsic structure of the data. Moreover, the optimization of the EnOCSVM can be solved using the standard SVM implementation similar to OCSVM, and all the advantages of SVM are preserved. Experiment results on benchmark data sets show that EnOCSVM really has better generalization than OCSVM and MOCSVM significantly.