Outlier detection using humoral-mediated clustering (HAIS)

Outlier detection has important applications in various data mining domains such as fraud detection, intrusion detection, customers´ behavior and employees´ performance analysis. Outlier detection is the process of detecting data objects which are significantly different from the rest of the data. In this paper, a novel cluster-based outlier detection method is proposed using humoral-mediated clustering algorithm (HAIS) which is based on humoral mediated response triggered in natural immune systems. The proposed method finds meaningful clusters as well as outliers simultaneously. This is an iterative approach where only clusters above threshold (larger sized clusters) are carried forward to the next cycle while removing small sized clusters. The validation of this method is conducted through widely used datasets from the literature as well as on simulated data.