Many research works in the area of outlier detection are focused on the so called ``density-based' methods. Such kind of methods can counter-act many drawbacks of the traditional outlier detection methods. However, most existing density-based methods use geometric-distance-based approaches to estimate the data point's local density, which leads to incorrect results in certain cases. To resolve the problem, the traditional local density estimation method is substituted by a neighborhood-chain-based method, and a new outlier detection method is proposed. Compared to the local outlier factor (LOF) and several of related modifications, the proposed one can find the outliers more accurately.