A Survey On Outlier Detection Methods In Spatio-Temporal Datasets (original) (raw)
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Spatio-Temporal Outlier Detection Techniques
The improvement in mobile computing techniques has generated massive spatio-temporal data. Mining spatiotemporal data and especially spatio-temporal outlier detection is an attractive and challenging topic that fascinated many researchers. Spatio-temporal outlier detection is used widely in several applications, including climate conditions to detect abnormal changes in the weather, urban safety management, transportation, and traffic management. In this paper, we review the methods of spatio-temporal outlier detection and classified them into four main categories: distance/densitybased outlier detection method, pattern outlier detection method, supervised or semi-supervised learning method, and statistics or probabilistic method. This classification is based on the technique used for identifying outliers. We also show when each approach was employed.
Spatio-temporal outlier detection in large databases
Journal of Computing and Information Technology, 2004
Outlier detection is one of the major data mining meth-ods. This paper proposes a three-step approach to detect spatio-temporal outliers in large databases. These steps are clustering, checking spatial neighbors, and checking temporal neighbors. In this paper, we introduce a new ...
IRJET- Spatio-Temporal Outlier Detection Techniques
IRJET, 2021
The improvement in mobile computing techniques has generated massive spatio-temporal data. Mining spatiotemporal data and especially spatio-temporal outlier detection is an attractive and challenging topic that fascinated many researchers. Spatio-temporal outlier detection is used widely in several applications, including climate conditions to detect abnormal changes in the weather, urban safety management, transportation, and traffic management. In this paper, we review the methods of spatio-temporal outlier detection and classified them into four main categories: distance/densitybased outlier detection method, pattern outlier detection method, supervised or semi-supervised learning method, and statistics or probabilistic method. This classification is based on the technique used for identifying outliers. We also show when each approach was employed.
Spatio-temporal outlier detection algorithms based on computing behavioral outlierness factor
Data & Knowledge Engineering, 2017
A major task in spatio-temporal outlier detection is to identify objects that exhibit abnormal behavior either spatially, and/or temporally. There have only been a few algorithms proposed for detecting spatial and/or temporal outliers. One example is the Local Density-Based Spatial Clustering of Applications with Noise (LDBSCAN). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is mainly for clustering; it just tells us whether an object belongs to a cluster or it is an outlier. A measure known as Local Outlier Factor (LOF) gives a quantitative measure of outlierness to each object, where a high LOF score means it is potentially an outlier. LDBSCAN algorithm, which combines the above notions, considers only the spatial context. Furthermore, the notion of a cluster is defeated (i.e. LDBSCAN may report clusters having less than the minimum required points in a cluster), and some of the outliers may not be detected because of the limitation of the existing conditions in the LDBSCAN algorithm. In this paper, we propose two algorithms, namely Spatio-Temporal Behavioral Density-based Clustering of Applications with Noise (ST-BDBCAN) and Approx-ST-BDBCAN. ST-BDBCAN algorithm adopts the proposed, new concept, called Spatio-Temporal Behavioral Outlier Factor (ST-BOF), which is a spatio-temporal extension to LOF. It also uses both spatial and temporal attributes simultaneously to define the context. By doing so, the relative importance of spatial continuity or temporal continuity appropriate to the application at hand can be established. The Approx-ST-BDBCAN algorithm achieves improved scalability, with minimal loss of detection accuracy by partitioning data points for parallel processing. Experimental results on synthetic, and buoy datasets suggest that our proposed algorithms are accurate and computationally efficient. Additionally, new Outlier Association with Hurricane Intensity Index (OAHII) measures are introduced for quantitative evaluation of the results from buoy dataset.
Spatial Outlier Detection Approaches and Methods: A Survey
Abstract: An outlier is any object which is inconsistent with the remaining objects in a database in data mining the outlier detection playsan interesting and important role because the removal of false outliers may affect the mined results to a greater extent if it is important information needed for analysis. The Spatial outliers are locations which are significantly different from theirneighborhoods even though they are not much deviated from the entire population. It helps in finding out the local instabilities ofobjects when compared with other objects in spatial data. The spatial exploration becomes important because it is applied in many applications like weather prediction, clinical traits, geospaced information processing etc.The detection of spatial outliers is necessary for analysis in this area. This paper presents a survey and study of spatial outliers, its approaches, detectionmethods and algorithms with their complexity along with their pros and cons. Key words: Outliers, approaches, methods, algorithms
A Rough Set Approach to Outlier Detection in Spatio Temporal Data
2011
Spatio-temporal data mining is a growing research area dedicated to the development of algorithms and computational techniques for the analysis of large spatio-temporal databases and the disclosure of interesting and hidden knowledge in these data, mainly in terms of periodic hidden patterns and outlier detection. In this thesis, the attention has been focalized on outlier detection in spatio-temporal data. Indeed, detecting outliers which are grossly different from or inconsistent with remaining data is a major challenge in real-world knowledge discovery and data mining applications. Nowadays, the high availability of data gathered from wireless sensor networks and telecommunication systems (such as GPS, GSM), that daily generate terabytes of data, has focalized the research attention on the interesting knowledge that can be gained from the analysis of spatio-temporal data. Spatio-temporal data are constituted by sampled locations at specific timestamps, tipically this kind of data deal with trajectory of moving objects that change their locations over time. The management and analysis of these data is interesting because undetected correlations between phenomena could be discovered and adequate improvements could be taken in many different fields, such as problem prevention, traffic management, discovery of meaningful behaviour pattern or accessibility of restricted areas and so on. In this thesis, we face an unsupervised outlier detection problem in an unlabeled spatio-temporal data. Two main research contributions are reported in the following two main parts of this thesis. In the first part of this thesis, we describe the first research contribution that consists of two non parametric methods. Most current methods for outlier detection give a binary classification of objects: is or is not an outlier or, but for many scenarios, it is more meaningful to assign to each object a degree of being an outlier (degree of outlier-obtained results. In particular, we want to show the advantages of considering this new set. Indeed, we compare the Rough Outlier Set extracted by the entire data set (our Universe of the discourse) and the Rough Outlier Set extracted by the Kernel Set.
The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial attribute values are significantly different from the values of their spatial neighbors. These objects, called spatial outliers, may reveal important phenomena in a number of applications including traffic control, satellite image analysis, weather forecast, and medical diagnosis. Most of the existing spatial outlier detection algorithms mainly focus on identifying single attribute outliers and could potentially misclassify normal objects as outliers when their neighborhoods contain real spatial outliers with very large or small attribute values. In addition, many spatial applications contain multiple non-spatial attributes which should be processed altogether to identify outliers. To address these two issues, we formulate the spatial outlier detection problem in a general way, design two robust detection algorithms, one for single attribute and the other for multiple attributes, and analyze their computational complexities. Experiments were conducted on a real-world data set, West Nile virus data, to validate the effectiveness of the proposed algorithms.
Spatio-temporal Outlier Detection in Precipitation Data
2008
The detection of outliers from spatio-temporal data is an important task due to the increasing amount of spatio-temporal data available, and the need to understand and interpret it. Due to the limitations of previous data mining techniques, new techniques to detect spatio-temporal outliers need to be developed. In this paper, we propose a spatio-temporal outlier detection algorithm called Outstretch. To apply this algorithm, we first need to discover the top-k outliers (high discrepancy regions) for each time period. For this task, we have extended the Exact-Grid and Approx-Grid algorithms developed by Agarwal et al.
Spatial Outlier Detection Algorithm for Trajectory Data
International journal of pure and applied mathematics, 2018
Trajectories are spatiotemporal data generated by moving objects containing the spatial position of object at various time intervals. GPS devices record this information and it is possible to construct trajectory of moving objects for analysis. Outlier analysis of trajectories is done to identify abnormal activities like intrusion detection, fraud detection, fault detection and rate event detection. In this paper, Trajectory Outlier Detection algorithm using Boundary (TODB) is proposed using a boundary construction algorithm and a binary classifier. In TODB, Convex Hull algorithm is used to construct the boundary and ray casting algorithm is used to build the binary classifier. TODB is tested for its accuracy using real world data sets. Experimental results on real world data sets demonstrate that TODB correctly classify normal and outlier trajectories. Keywords—GPS,Trajectory; Spatial Outlier Detection; Convex hull algorithm;Classification
A Survey for Different Approaches of Outlier Detection in Data Mining
— 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.