Outliers are unusual data values that are inconsistent with most of the records. Such non-representative records can seriously affect the model to be produced, so detecting outlier is a significant job to achieve higher accuracy. Several outlier detection methods are used in literature for real as well as simulated data sets. The aim of this study is to compare the two outlier detection method ...

Outliers can significantly affect data mining performance. Outlier mining is an important issue in knowledge discovery and data mining and has attracted increasing interests in recent years. Class outlier is promising research direction. Few researches have been done in this direction. The paper theme has two main goals: the first one is to show the significance of Class Outlier Mining by discu...

Outlier detection has been a very important concept in data mining. The aim of outlier detection is to find those objects that are of not the norm. There are many applications of outlier detection from network security to detecting credit fraud. However most of the outlier detection algorithms are focused towards numerical data and do not perform well when applied to categorical data. In this p...

In many real-world applications, data come with corruptions, large errors or outliers. One popular approach is to use -norm function. However, the robustness of -norm function is not well understood so far. In this paper, we present a new outlier regularization framework to understand and analyze the robustness of -norm function. There are two main features for the proposed outlier regularizati...

We introduce a generalization of differential privacy called tailored differential privacy, where an individual’s privacy parameter is “tailored” for the individual based on the individual’s data and the data set. In this paper, we focus on a natural instance of tailored differential privacy, which we call outlier privacy : an individual’s privacy parameter is determined by how much of an “outl...

In the field of wireless sensor networks, the measurements that deviate from the normal behaviour of sensed data are taken to be as outliers. The potential sources of outliers can be noise and errors, events, and malicious attacks on the network. This paper give an overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Also, a technique-based ...

BACKGROUND Using hybrid approach for gene selection and classification is common as results obtained are generally better than performing the two tasks independently. Yet, for some microarray datasets, both classification accuracy and stability of gene sets obtained still have rooms for improvement. This may be due to the presence of samples with wrong class labels (i.e. outliers). Outlier dete...

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 ...

Accurate location information is critical to many applications in wireless sensor networks (WSNs) such as target tracking, environmental monitoring and geographical routing. Localization aims to figure out the locations of unknown nodes based on global locations of anchors and inter-node distance measurements. However, the existence of outlier anchors and outlier distances degrade localization ...

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from...