In their paper, Davies and Gather (1993) formalized the task of outlier identiica-tion, considering also certain performance criteria for outlier identiiers. One of those criteria, the maximum asymptotic bias, is carried over here to multivariate outlier identiiers. We show how this term depends on the respective biases of estimators which are used to construct the identiier. It turns out that ...

Outlier detection in mixed attribute datasets has proved to be a challenging task required in real world applications. Most existing algorithms for outlier detection do not consider the interactions between categorical and numerical attributes. The Pattern based Outlier Detection (POD) algorithm (Zhang & Jin, 2011), has had considerable success in the detecting outliers by analysing such intera...

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...

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...