Outlier detection is a challenging problem due to the complexity of real-life data. Specifically, an effective outlier detection method should be able to handle (1) different types of outliers: local outliers, global outliers, and cluster outliers; (2) a lack of prior knowledge of outlier number or percentage; (3) neighboring clusters with different densities; and (4) real-time detection. Unfortunately, few algorithms can tackle all these challenges simultaneously. In this paper, we propose a chain-based theory to address these issues, where a minority of data points will be considered outliers if their distances from normal data points change abruptly. We present the parallel chaining method based on this theory. Experiments demonstrate that the proposed method exhibits comparable performance to other state-of-the-art methods in the synthetic datasets and outperform other methods in the real-life datasets.