Many studies of outlier detection have been developed based on the cluster-based outlier detection approach, since it does not need any prior knowledge of the dataset. However, the previous studies only regard the outlier factor computation with respect to a single point or a small cluster, which reflects its deviates from a common cluster. Furthermore, all objects within outlier cluster are as...

This study is about outlier detection in time series data. The main objective is to derive and to test statistics for detecting additive outlier (AO) and innovative outlier (IO) in GARCH(1,1) processes and subsequently to develop a procedure for testing the presence of outliers using the statistics. A test statistic has been derived for each type of outlier. In the derivation of the statistics,...

Outlier is a data point that deviates too much from the rest of dataset. Most of real-world dataset have outlier. Outlier analysis is one of the techniques in data mining whose task is to discover the data which have an exceptional behavior compare to remaining dataset. Outlier detection plays an important role in data mining field. Outlier Detection is useful in many fields like Medical, Netwo...

Analyzing exceptional objects is an important mining task. It includes the identification of outliers but also the description of outlier properties in contrast to regular objects. However, existing detection approaches miss to provide important descriptions that allow human understanding of outlier reasons. In this work we present OutRules, a framework for outlier descriptions that enable an e...