Empirical performance analysis of two algorithms for mining intentional knowledge of distance-based outliers

This thesis studies the empirical analysis of two algorithms, Uplattice and Jumplattice for mining intentional knowledge of distance-based outliers [19]. These algorithms detect strongest and weak outliers among them. Finding outliers is an important task required in major applications such as credit-card fraud detection, and the NHL statistical studies. Datasets of varying sizes have been tested to analyze the empirical values of these two algorithms. Effective data structures have been used to gain efficiency in memory-performance. The two algorithms provide intentional knowledge of the detected outliers which determines as to why an identified outlier is exceptional. This knowledge helps the user to analyze the validity of outliers and hence provides an improved understanding of the data.