Reverse Accessible in Local Outlier Factor Density Based Recognition

Abstract

Recent data mining outlier to recognition data point the expected system to sufficient dataset or is significantly many data exhibits that as dimensionality increases there exists hubs and anti hubs the points that frequently occur in k nearest neighbor lists. Ant hubs are points that infrequently model in kNN lists. .This proposed system to developing and comparing to  unsupervised outlier detection models This proposed method to details about the development and analysis of outlier detection methods is Local Outlier Factor (LOF), and  Local Distance-Based Outlier Factor(LDOF) .Outliers improves the results of the previous systems to reference to speed, complexity and efficiency . The classification algorithms is used to finding the relevant features and classify in the criteria in data mining methods. These techniques suffer to increasing complexity, size and variety of data sets. The proposed incremental LOF algorithm takes equivalent finding performance as the iterated static LOF algorithm while requiring significantly less computational time. In addition, the incremental LOF algorithm is dynamically modify the data of data points. This is a very important application, change data profiles to change over time. Moreover, we have also given a broad comparison of the number of model the different outlier factors.

Index Terms:  Clustering-based; Density-based and Model-based approaches; Nearest Neighbour; Outlier Detection; Discrimination; Outliers; data mining; Clustering; Neural Network.