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...

Outlier detection is a task that finds objects that are considerably dissimilar, exceptional or inconsistent with respect to the remaining data. Outlier detection has wide applications which include data analysis, financial fraud detection, network intrusion detection and clinical diagnosis of diseases. In data analysis applications, outliers are often considered as error or noise and are remov...

Outlier detection in data streams is an immensely enthralling problem in many application areas such as network intrusion detection, faulty sensor detection, fraud detection in online financial transactions etc. Majority of existing outlier detection techniques have been mainly designed for static datasets and require a global view and multiple scans of data which is not feasible in case of str...

This paper presents an application of a local density based outlier detection method in compliance in the context of public health service management. Public health systems have consumed a significant portion of many governments’ expenditure. Thus, it is important to ensure the money is spent appropriately. In this research, we studied the potentials of applying an outlier detection method to m...

The term “outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous application...

Outlier detection is very interesting, useful and challenging problem in the field of data mining. Because of sparse data clustering algorithm which are based on distance will not work to find outliers in spatial data. Problem of finding irregular feature in spatial data need to be explore. Many existing approaches have been proposed to overcome the problem of outlier detection in spatial Geogr...

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, Fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different to the most of the individuals of the...

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different from the most of the individuals of t...

The term “outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous application...