A RNN Novel Approach for Unsupervised Distance-Based Outlier Detection
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 7
Abstract
Detection of outliers in data defined as finding patterns in data that do not conform to normal behavior or data that do not conformed to expected behavior, such a data are called as outliers, anomalies, exceptions. Anomaly and Outlier have similar meaning. The analysts have strong interest in outliers because they may represent critical and actionable information in various domains, such as intrusion detection, fraud detection, and medical and health diagnosis. An Outlier is an observation in data instances which is different from the others in dataset. There are many reasons due to outliers arise like poor data quality, malfunctioning of equipment, ex credit card fraud. Data Labels associated with data instances shows whether that instance belongs to normal data or anomalous. Based on the availability of labels for data instance, the anomaly detection techniques operate in one of the three modes are 1)Supervised Anomaly Detection, techniques trained in supervised mode consider that the availability of labeled instances for normal as well as anomaly classes in a a training dataset. 2) Semi-supervised Anomaly Detection, techniques trained in supervised mode consider that the availability of labeled instances for normal, do not require labels for the anomaly class. 3) Unsupervised Anomaly Detection, techniques that operate in unsupervised mode do not require training data. There are various methods for outlier detection based on nearest neighbors, which consider that outliers appear far from their nearest neighbors. Such methods base on a distance or similarity measure to search the neighbors, with Euclidean distance. Many neighbor-based methods include defining the outlier score of a point as the distance to its kthnearest neighbor (k-NN method), some methods that determine the score of a point according to its relative density, since the distance to the kth nearest neighbor for a given data point can be viewed as an estimate of the inverse density around it.
Authors and Affiliations
M. Siva Kumar, G. Prasadbabu
Keywords
Related Articles
- EP ID EP22441
- DOI -
- Views 261
- Downloads 3
Forward and Backward Sweep Algorithm for Distribution Power Flow Analysis and Comparison of Different Load Flow Methods.
Power flow analysis is a very important and fundamental tool for the analysis of any electrical distribution system and is used in the operational as well as planning stages. Certain applications particularly in distrib...
Review Paper on Data Access Control using CPABE for Multi-Authority Cloud Storage System
Cloud computing is the system on which we can store data over a network and easily access it from anywhere. But in the case of public cloud storage systems, access control is a most concerning issue[4]. Cipher-text-Poli...
Finite Element Analysis of Kevlar Reinforced Rubber as Seismic Isolator
The seismic isolators are one of the modern innovative solution for building protection against the seismic behaviour of earth. Using the scope of composite materials we can create better solution for engineering proble...
A Novel Work for Improving Security and Challenges using Fuzzy Logic Approach in VANETS
Vehicular AdHoc networks are the most emerging technologies in now-a-days. VANETs have many challenges like security and time latency when users are travelling in the roadways. There are many techniques available to ove...
Soil Stabilization Using Coconut Coir Fibre
Use of Coconut coir Fibre for improving soil property is advantageous because they are cheap, locally available and eco-friendly. In this study, the stabilizing effect of Coconut coir Fibre (Natural Fibre) on soil prope...