A fundamental challenge for researchers studying the brain is to explain how distributed patterns of brain activity relate to a specific representation or computation. Multivariate techniques are therefore becoming increasingly popular for pattern localization of functional magnetic resonance imaging (fMRI) data. The increased power of these techniques can be offset by their susceptibility to m...

Penelitian ini bertujuan mengindentifikasi outlier (pencilan) dan kenormalan data pada univariat data. Adapun yang digunakan berupa persentase kemiskinan di Indonesia tahun 2022 berasal dari Badan Pusat Statistik. Metode pengujian dilakukan dengan menggunakan grafik box plot, histrogram uji Grubbs. Sedangkan menggunkan SK Test Shapiro Wilk. Hasil penelitian menunjukkan terdapat yaitu observasi ...

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 ...