Outlier Detection Using Ball Descriptions with Adjustable Metric

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Outlier Detection is a critical and cardinal research task due its array of applications in variety of domains ranging from data mining, clustering, statistical analysis, fraud detection, network intrusion detection and diagnosis of diseases etc. Over the last few decades, distance-based outlier detection algorithms have gained significant reputation as a viable alternative to the more traditio...

On Some Properties of Analytic Spaces Connected with Bergman Metric Ball

Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples

For natural language processing on machines, resolving such peculiar usages would be particularly useful in constructing a dictionary and dataset for word sense disambiguation. Hence, it is necessary to develop a method to detect such peculiar examples of a target word from a corpus. Note that, hereinafter, we define a peculiar example as an instance in which the target word or phrase has a new...

Identification of outliers types in multivariate time series using genetic algorithm

Multivariate time series data, often, modeled using vector autoregressive moving average (VARMA) model. But presence of outliers can violates the stationary assumption and may lead to wrong modeling, biased estimation of parameters and inaccurate prediction. Thus, detection of these points and how to deal properly with them, especially in relation to modeling and parameter estimation of VARMA m...

Initialization of K-modes clustering using outlier detection techniques

The K-modes clustering has received much attention, since it works well for categorical data sets. However, the performance of K-modes clustering is especially sensitive to the selection of initial cluster centers. Therefore, choosing the proper initial cluster centers is a key step for K-modes clustering. In this paper, we consider the initialization of K-modes clustering from the view of outl...