In this paper, a new approach for autonomous anomaly detection is introduced within the Empirical Data Analytics (EDA) framework. This approach is fully data-driven and free from thresholds. Employing the nonparametric EDA estimators, the proposed approach can autonomously detect anomalies in an objective way based on the mutual distribution and ensemble properties of the data. The proposed approach firstly identifies the potential anomalies based on two EDA criterions, and then, partitions them into shape-free non-parametric data clouds. Finally, it identifies the anomalies in regards to each data cloud (locally). Numerical examples based on synthetic and benchmark datasets demonstrate the validity and efficiency of the proposed approach.