Comparative Analysis of Proximity Based Outlier Detection Algorithms
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Department of Computer Science and Information Technology
Abstract
Outlier detection is the process of finding peculiar pattern from given set of data. Nowadays,
outlier detection is more popular subject in different knowledge domain. Data size is rapidly
increases every year there is need to detect outlier in large data set as early as possible.
In this research, comparison of three different proximity based outlier detection algorithm i.e.
distance based method (LDOF), cluster-based method (K-medoid based OD) and density based
method (LOF) is presented. The main aim of this research is to evaluate their performance of
those three different proximity based outlier algorithm for different data set with different
dimension. The data set used for this research are chosen such way that they are different in size,
mainly in terms of number of instances and attributes. When comparing the performance of all
three proximity based outlier detection algorithms, density based method (LOF) is found to be
better algorithm to detect outlier in most cases with accuracy level 94.47% as well as 66.93%
precision, 83.14% recall and 73.18% F-measure value.