Minimal Spanning Tree Algorithm Applied to the Implementation of an Efficient Isolation Forest for Anomaly Detection

This study introduces innovative anomaly detection algorithms based on the Isolation Forest (IF) method. It begins by presenting an overview of the anomaly detection issue, encompassing a review of general methods used in this field. Subsequently, the IF method is discussed in detail, along with key concepts necessary for the implementation of the novel techniques. Studies regarding modifications of the IF method are analyzed. Within this review, particular emphasis is placed on those publications focused on the improvements and extensions of the basic IF algorithm. The study includes the introduction of five new anomaly detection algorithms. Among them are two attribute reduction methods used in the data preprocessing, based on clustering techniques such as