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...

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 ...

In recent years, many new techniques have been developed for mining and managing uncertain data. This is because of the new ways of collecting data which has resulted in enormous amounts of inconsistent or missing data. Such data is often remodeled in the form of uncertain data. In this paper, we will examine the problem of outlier detection with uncertain data sets. The outlier detection probl...

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...

PURPOSE There is a clear need to improve risk stratification and to identify novel therapeutic targets in aggressive prostate cancer. The goal of this study was to investigate genes with outlier expression with prognostic association in high-risk prostate cancer patients as potential biomarkers and drug targets. EXPERIMENTAL DESIGN We interrogated microarray gene expression data from prostate...

Analyzing exceptional objects is an important mining task. It includes the identification of outliers but also the description of outlier properties in contrast to regular objects. However, existing detection approaches miss to provide important descriptions that allow human understanding of outlier reasons. In this work we present OutRules, a framework for outlier descriptions that enable an e...

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 ...

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...