A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Outlier detection <s> Abstract Characteristics of observations which cause them to be influential in a least squares analysis are investigated and related to residual variances, residual correlations, and the convex hull of the observed values of the independent variables. It is shown how deleting an observation can substantially alter an analysis by changing the partial F-tests, the studentized residuals, the residual variances, the convex hull of the independent variables, and the estimated parameter vector. Outliers are discussed briefly, and an example is presented. <s> BIB001 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Outlier detection <s> For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical. <s> BIB002 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Outlier detection <s> Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labeled training data. This training data is 90typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. In this paper, we have discussed anomaly based instruction detection, pros and cons of anomaly detection, supervised and unsupervised anomaly detection. <s> BIB003 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Outlier detection <s> Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with. <s> BIB004 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Outlier detection <s> The discussions in the previous chapters focus on the problem of unsupervised outlier detection in which no prior information is available about the abnormalities in the data. In such scenarios, many of the anomalies found correspond to noise or other uninteresting phenomena. It has been observed [338, 374, 531] in diverse applications such as system anomaly detection, financial fraud, and Web robot detection that interesting anomalies are often highly specific to particular types of abnormal activity in the underlying application. In such cases, an unsupervised outlier detection method might discover noise, which is not specific to that activity, and therefore may not be of interest to an analyst. In many cases, different types of abnormal instances could be present, and it may be desirable to distinguish among them. For example, in an intrusion-detection scenario, different types of intrusion anomalies are possible, and the specific type of an intrusion is important information. <s> BIB005 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Outlier detection <s> Outliers once upon a time regarded as noisy data in statistics, has turned out to be an important problem which is being researched in diverse fields of research and application domains. Many outlier detection techniques have been developed specific to certain application domains, while some techniques are more generic. Some application domains are being researched in strict confidentiality such as research on crime and terrorist activities. The techniques and results of such techniques are not readily forthcoming. A number of surveys, research and review articles and books cover outlier detection techniques in machine learning and statistical domains individually in great details. In this paper we make an attempt to bring together various outlier detection techniques, in a structured and generic description. With this exercise, we hope to attain a better understanding of the different directions of research on outlier analysis for ourselves as well as for beginners in this research field who could then pick up the links to different areas of applications in details. <s> BIB006
Using domain-related additional information provided by the experts, like the normal range of values of the variables, supervised outlier detection can be conducted BIB005 . When this information is totally, or partially, available  , it is convenient to take advantage of it. In front of a lack of this domain-related information, unsupervised outlier detection must be used. Graphical techniques were once the most common method for unsupervised outlier detection, but increases in database sizes and dimensionality have led to a variety of automated techniques BIB006 . The use of standard deviations is possible when considering variables following symmetric distribution; but outliers may also take the form of unusual combinations of two or more variables, as said before, and then, they only manifest in the proper dimensionality subspace. This means that graphical representation will not be useful over more than three dimensions. In that case, hierarchical clustering, where outliers form singletons BIB003 or other methods BIB002 might help. Also, some specific statistical tests exist  but they are only implemented in some software tools, and usually assume some specific univariate distributions. In any case, the data point should be analyzed as a whole to understand the nature of the outlier and multivariate approach is required. See BIB004 for a survey in the field. Determining whereas an outlier is performing as an influent observation can be quantified by the Cook's D coefficient BIB001 and it is important to determine if the observation belongs to the target population or not.