Outlier detection is an important process for text document collections, but as the collection grows, the detection process becomes a computationally expensive task. Random projection has shown to provide a good fast approximation of sparse data, such as document vectors, for outlier detection. The random samples of Fourier and cosine spectrum have shown to provide good approximations of sparse...

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...

Is particularly useful for high dimensional data where outliers cannot be found.High dimensional data in Euclidean space pose special challenges to data. In about just the last few years, the task of unsupervised outlier detection has found.Outlier detection is an outstanding data mining task referred to open pdf with mac word class="text" href="https://tokiqivy.files.wordpress.com/2015/06/opel...

Outlier detection is a data mining method for discovering exceptional, abnormal or suspiciously unusual samples in a data set. Outliers typically represent the data rich but information poor dilemma. Data mining methods are applied to solve this problem in broad range of application fields like credit card fraud detection, network intrusion detection, error extraction, clinical disease research...

Outlier detection is a primary step in many data-mining applications. We present several methods for outlier detection, while distinguishing between univariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for data mining is often based on dist...

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...

Outlier detection is a fundamental step in knowledge discovery in databases. With the increasing number of high-dimensional databases, existing outlier detection algorithms that work only in the context of full space are unable to effectively screen out informative outliers. This is because majority of these outliers exists only in subspaces. In this paper, we identify a new outlier detection t...

Outlier detection has significant importance in the data mining domain. Applications which contain streaming data flow may have many abnormal or outlier data and these applications require efficient outlier detection techniques to detect and analyze these abnormal patterns. Outlier detection is the process of detecting patterns in the data which do not adhere to the normal behavior or data. The...

In this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray data analysis. It is important to identify outliers in order to explore underlying experimental or biological problems and remove erroneous data....

In this paper, an outlier detection method based on radial basis functions–principal component analysis (RBF-PCA) approach and Prescott method, a statistical detection approach, is proposed to detect the outlier in the complex system without clear mechanisms. Making full use of the capacity of neural networks on nonlinear mapping and the effect of Prescott method on outlier detection in linear ...