Low quality sensor data limits WSN capabilities for providing reliable real-time situationawareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of c...

In this paper we have made a review of various outlier detection techniques from data mining perspective. Existing studies in data mining focus generally on finding patterns from large datasets and using it for organizational decision making. However, finding exceptions and outliers did not receive much attention in the data mining field as other topics received. Finally, this paper concludes s...

In this paper we introduce weighted estimators of the location and dispersion of a multivariate data set with weights based on the ranks of the Mahalanobis distances. We discuss some properties of the estimators like the breakdown point, influence function and asymptotic variance. The outlier detection capacities of different weight functions are compared. A simulation study is given to investi...

Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density ...

The FC-ANN (Artificial Neural Network) is used to speed up the technique. The anomaly Outlier detection is primary in various data-mining applications. Outlier detection methods have been suggested for number of application such as, fraud detection, voting irregularity analysis, data cleansing, clinical trials, network intrusion, severe weather prediction, geographic information system, credit ...

Outlier detection is an integral component of statistical modelling and estimation. For highdimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the...

Outlier detection is a data analysis method and has been used to detect and remove anomalous observations from data. In this paper, we firstly introduced some current mainstream outlier detection methodologies, i.e. statistical-based, distance-based, and density-based. Especially, we analyzed distance-based approach and reviewed several kinds of peculiarity factors in detail. Then, we introduce...

The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simul...

Outlier detection is an important task in data mining and its applications. It is defined as a data point which is very much different from the rest of the data based on some measures. Such a data often contains useful information on abnormal behavior of the system described by patterns. In this paper, a novel method for outlier detection is proposed among inconsistent dataset. This method expl...

Outlier detection in multimedia database is an emerging problem in many applications such as medical image archive and video surveillance investigation. Unlike text database, multiple modalities typically exist in multimedia databases. In this paper, we propose a probabilistic model to detect outliers in image database. This model integrates distance based outlier detection in textual metadata ...