ARDV: A New Density Based Outlier Mining Approach 1

Lecture Notes in Computer Science, 2011

In this paper, we propose a nearest neighbor based outlier detection algorithm, NDoT . We introduce a parameter termed as N earest N eighbor F actor (NNF ) to measure the degree of outlierness of a point with respect to its neighborhood. Unlike the previous outlier detection methods NDoT works by a voting mechanism. Voting mechanism binarizes the decision compared to the top-N style of algorithms. We evaluate our method experimentally and compare results of NDoT with a classical outlier detection method LOF and a recently proposed method LDOF . Experimental results demonstrate that NDoT outperforms LDOF and is comparable with LOF .

Outlier Detection is one of the most important topics in data mining and knowledge discovery in databases (KDD). It is to find a methodology to detect instances in a dataset that do not conform to an established normal behavior. There are some researches to find outliers, for example Local Outlier Factor

IEEE Transactions on Knowledge and Data Engineering, 2006

A distance-based outlier detection method that finds the top outliers in an unlabeled data set and provides a subset of it, called outlier detection solving set, that can be used to predict the outlierness of new unseen objects, is proposed. The solving set includes a sufficient number of points that permits the detection of the top outliers by considering only a subset of all the pairwise distances from the data set. The properties of the solving set are investigated, and algorithms for computing it, with subquadratic time requirements, are proposed. Experiments on synthetic and real data sets to evaluate the effectiveness of the approach are presented. A scaling analysis of the solving set size is performed, and the false positive rate, that is, the fraction of new objects misclassified as outliers using the solving set instead of the overall data set, is shown to be negligible. Finally, to investigate the accuracy in separating outliers from inliers, ROC analysis of the method is accomplished. Results obtained show that using the solving set instead of the data set guarantees a comparable quality of the prediction, but at a lower computational cost.

International Journal of Computer Applications, 2012

Outlier detection is a fundamental issue in data mining, specifically it has been used to detect and remove anomalous objects from data.mining. The proposed approach to detect outlier includes three methods which are clustering, pruning and computing outlier score. For clustering k-means algorithm is used which partition the dataset into given number of clusters. In pruning, based on some distance measure, points which are closed to centroid of each cluster are pruned. For the unpruned points, local distance based outlier factor (LDOF) measure is calculated. A measure called LDOF, tells how much a point is deviating from its neighbors. The high LDOF value of a point indicates that the point is deviating more from its neighbors and probably it may be an outlier.

International Journal of Computer Applications, 2013

Outlier detection is an extremely important problem with direct application in a wide variety of domains. A key challenge with outlier detection is that it is not a wellformulated problem like clustering. In this paper, discussion on different techniques and then comparison by analyzing their different aspects, essentially, time complexity. Every unique problem formulation entails a different approach, resulting in a huge literature on outlier detection techniques. Several techniques have been proposed to target a particular application domain. The classification of outlier detection techniques based on the applied knowledge discipline provides an idea of the research done by different communities and also highlights the unexplored research avenues for the outlier detection problem. Discussed of the behavior of different techniques will be done, in this paper, with respect to the nature. The feasibility of a technique in a particular problem setting also depends on other constraints. For example, Statistical techniques assume knowledge about the underlying distribution characteristics of the data. Distance based techniques are typically expensive and hence are not applied in scenarios where computational complexity is an important issue.

Outlier (or anomaly) detection is an important problem for many domains, including fraud detection, risk analysis, network intrusion and medical diagnosis, and the discovery of significant outliers is becoming an integral aspect of data mining. This paper presents CURIO, a novel algorithm that uses quantisation and implied distance metrics to provide a fast algorithm that is linear for the number of objects and only requires two sequential scans of disk resident datasets. CURIO includes a novel direct quantisation technique and the explicit discovery of outlier clusters. Moreover, a major attribute of CURIO is its simplicity and economy with respect to algorithm, memory footprint and data structures.

— Outlier is defined as an event that deviates too much from other events. The identification of outlier can lead to the discovery of useful and meaningful knowledge. Outlier means it's happen at some time it's not regular activity. Research about Detection of Outlier has been extensively studies in the past decade. However, most existing research focused on the algorithm based on specific knowledge, compared with outlier detection approach is still rare. In this paper mainly focused on different kind of outlier detection approaches and compares it's prone and cones. In this paper we mainly distribute of outlier detection approach in two parts classic outlier approach and spatial outlier approach. The classical outlier approach identifies outlier in real transaction dataset, which can be grouped into statistical approach, distance approach, deviation approach, and density approach. The spatial outlier approach detect outlier based on spatial dataset are different from transaction data, which can be categorized into spaced approach and graph approach. Finally, the comparison of outlier detection approaches.

IEEE Access

Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000-2019. Firstly, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-ofthe-art outlier detection methods and further discuss them in detail in terms of their performance. Secondly, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

This paper presents a new approach for detecting outliers by introducing the notion of object’s proximity. The main idea is that normal point has similar characteristics with several neighbors. So the point in not an outlier if it has a high degree of proximity and its neighbors are several. The performance of this approach is illustrated through real datasets.

IEEE Access, 2021

This paper proposes a new outlier detection approach that measures the degree of outlierness for each instance in a given dataset. The proposed model utilizes a mass-based dissimilarity measure to address the weaknesses of neighbor-based outlier models while detecting local outliers in the dataset within a variety of data point densities. In particular, it first applies a hierarchical partitioning technique to generate a set of tree-like nested structure partitions for the input dataset, and then a mass-based dissimilarity measure is defined to quantify the dissimilarity between two data instances given the generated hierarchical partition structure. After that, for each data instance, a context set is obtained by gathering the neighbors around it with the k lowest mass dissimilarities, and based on those context sets, a mass-based local outlier score model is introduced to compute the outlierness for each individual instance. The proposed approach fundamentally changes the perspective of the outlier model by using the mass-based measurement instead of the distance-based functions used in most neighbor-based methods. A comprehensive experiment conducted on both synthetic and real-world datasets demonstrates that the proposed approach is not only competitive with the existing state-of-the-art outlier detection models but is also an efficient and effective alternative for local outlier detection methods. INDEX TERMS Outlier detection, mass-based dissimilarity, unsupervised learning, knowledge discovery.