Anomaly Detection is the process of finding outlying record from a given data set. This problem has been of increasing importance due to the increase in the size of data and the need to efficiently extract those outlying records which could indicate unauthorized access of the system, credit card theft or the diagnosis of a disease. The aim of this bachelor thesis is to implement a RapidMiner ex...

Outlier detection has recently become an important problem in many data mining applications. In this paper, a novel unsupervised algorithm for outlier detection is proposed. First we apply a provably globally optimal Expectation Maximization (EM) algorithm to fit a Gaussian Mixture Model (GMM) to a given data set. In our approach, a Gaussian is centered at each data point, and hence, the estima...

This article uses Projection Pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions could be more powerful than testing the multivariate series directly. The optimal directions for detecting outliers are found by numerical optimization of the kurtosis coefficient of the projected series. We pro...

Rousseeuw’s minimum covariance determinant (MCD) method is a highly robust estimator of multivariate mean and covariance. In practice, the MCD covariance estimator may be singular. However, a nonsingular covariance estimator is required to calculate the Mahalanobis distance. In order to fix this singular problem, we propose an improved version of the MCD estimator, which is a combination of the...

We analyse the interplay of density estimation and outlier detection in density-based outlier detection. By clear and principled decoupling of both steps, we formulate a generalization of density-based outlier detection methods based on kernel density estimation. Embedded in a broader framework for outlier detection, the resulting method can be easily adapted to detect novel types of outliers: ...

An outlier is an observation that deviates so much from other observations that it seems to have been generated by a different mechanism. Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that exhibit a behavior that is very different from most of the individuals of the data set. Fr...

Unsupervised anomaly detection is the process of finding outlying records in a given dataset without prior need for training. In this paper we introduce an anomaly detection extension for RapidMiner in order to assist non-experts with applying eight different nearest-neighbor and clustering based algorithms on their data. A focus on efficient implementation and smart parallelization guarantees ...