Detecting outliers from high-dimensional data is a challenge task since outliers mainly reside in various lowdimensional subspaces of the data. To tackle this challenge, subspace analysis based outlier detection approach has been proposed recently. Detecting outlying subspaces in which a given data point is an outlier facilitates a better characterization process for detecting outliers for high...

Outliers are very common in the environmental data monitored by a sensor network consisting of many inexpensive, low fidelity, and frequently failed sensors. The limited battery power and costly data transmission have introduced a new challenge for outlier cleaning in sensor networks: it must be done innetwork to avoid spending energy on transmitting outliers. In this paper, we propose an in-ne...

Outlier detection is an important research problem in data mining that aims to find objects that are considerably dissimilar, exceptional and inconsistent with respect to the majority data in an input database [50]. Outlier detection, also known as anomaly detection in some literatures, has become the enabling underlying technology for a wide range of practical applications in industry, busines...

We present a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into consideration. Experiments are conducted using both synthetic datasets and a real life construction equipm...

Outlier detection is a very important type of data mining, which is extensively used in application areas. The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data, but also uses lots of machine resources, which results in the imbalance of the machine load. This paper presents an distancebased outlier detection algorithm. These expe...

Under the assumption of that the variance-covariance matrix is fully populated, Baarda’s w-test is turn out to be completely different from the standardized least-squares residual. Unfortunately, this is not generally recognized. In the limiting case of only one degree of freedom, all the three types of test statistics, including Gaussian normal test, Student’s t-test and Pope’s Tau-test, will ...

Summarization requires selection of the more informative sentences within a set of documents. Generally, process assumes the document set includes related topics to a subject. However, some of the documents may be outlier and the effect of an outlier document might affect the success of extractive summary. Research is focused on filtering documents at the extraction stage these are outlier. Ext...

In this paper a novel Support vector clustering(SVC) method for outlier detection is proposed. Outlier detection algorithms have application in several tasks such as data mining, data preprocessing, data filter-cleaner, time series analysis and so on. Traditionally outlier detection methods are mostly based on modeling data based on its statistical properties and these approaches are only prefe...