Data Mining simply refers to the extraction of very interesting patterns of the data from the massive data sets. Outlier detection is one of the important aspects of data mining which actually finds out the observations that are deviating from the common expected behavior. Outlier detection and analysis is sometimes known as outlier mining. In this paper, we have tried to provide the broad and ...

Outlier detection is an important and attractive problem in knowledge discovery in large datasets. Instead of detecting an object as an outlier, we study detecting the n most outstanding outliers, i.e. the top-n outlier detection. Further, we consider the problem of combining the top-n outlier lists from various individual detection methods. A general framework of ensemble learning in the top-n...

We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for (OD) in functional data. FastMUOD detects outliers by computing, each curve, an amplitude, magnitude, shape index meant to target corresponding types outliers. Some methods adapting multivariate data are then proposed. These include applying on components using random projection...

Outlier detection is a very important type of data mining, which is extensively used in application areas. The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data, but also uses lots of machine resources, which results in the imbalance of the machine load. This paper presents an distancebased outlier detection algorithm. These expe...

In this paper a novel Support vector clustering(SVC) method for outlier detection is proposed. Outlier detection algorithms have application in several tasks such as data mining, data preprocessing, data filter-cleaner, time series analysis and so on. Traditionally outlier detection methods are mostly based on modeling data based on its statistical properties and these approaches are only prefe...

Outlier detection in mixed attribute datasets has proved to be a challenging task required in real world applications. Most existing algorithms for outlier detection do not consider the interactions between categorical and numerical attributes. The Pattern based Outlier Detection (POD) algorithm (Zhang & Jin, 2011), has had considerable success in the detecting outliers by analysing such intera...

Now day’s Outlier Detection is used in various fields such as Credit Card Fraud Detection, Cyber-Intrusion Detection, Medical Anomaly Detection, and Data Mining etc. So to detect anomaly objects from various types of dataset Outlier Detection techniques are used, that detects and remove the anomaly objects from the dataset. Outliers are the containments that divert from the other objects. Outli...