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https://airccse.com/lsij/index.html DETECTION OF OUTLIERS IN CIRCULAR DATA USING KERNEL DENSITY FUNCTION Hazem I. El Shekh Ahmed1, Ali H. Abuzaid2 and Ikhlas I. Al Awar 1Department of Mathematics, Al Quds Open University-Gaza, Palestine 2Department of Mathematics, Al-Azhar University-Gaza, Palestine ABSTRACT Background: Outlier detection has recently become an important problem in many industrial and financial applications. The proposal in this paper is based on detect an outlier in circular data by the local density factor (LDF). The name of local density estimate (LDE) is justified by the fact that we sum over a local neighborhood compared to the sum over the whole circular data commonly used to compute the kernel density estimate (KDE). Methods: We discuss new techniques for outlier detection which find the outliers by comparing the local density of each point to the local density of its neighbors in circular data. In our experiments, we performed simulated two data sets ...