Outlier Detection In Data Streams Research Articles

Leveraging the Christoffel function for outlier detection in data streams

Leveraging the Christoffel function for outlier detection in data streams

Efficient density and cluster based incremental outlier detection in data streams

In this paper, a novel, parameter-free, incremental local density and cluster-based outlier factor (iLDCBOF) method is presented that unifies incremental versions of local outlier factor (LOF) and density-based spatial clustering of applications with noise (DBSCAN) to detect outliers efficiently in data streams. The iLDCBOF has many advanced advantages compared to previously reported iLOF-based studies: (1) it is based on a newly-developed core k-nearest neighbor (CkNN) concept to reliably and scalably detect outliers from data streams and prevent the clustering of outliers; 2) it uses a newly-developed algorithm that automatically adjusts the value of the k (number of neighbors) parameter for different real-time applications; and 3) it uses the Mahalanobis distance metric, so its performance is not affected even for large amounts of data. The iLDCBOF method is well suited for different data stream applications because it requires no distribution assumptions, it is parameterless (determined automatically), and it is easy to implement. ROC-AUC and statistical test analysis results from extensive experiments performed on 16 different real-world datasets showed that the iLDCBOF method significantly outperformed benchmark methods.

Review: Different approaches for Outlier Detection in Data Stream Mining

Stream data differs from regular data in that it is continuously generated by many applications, posing unique processing issues such as enormous, limitless, and idea drift. For the researcher, data mining was one of the most exciting fields of study. In today's world, people are attempting to extract more information from data with order to aid in day-to-day forecasting. Outlier detection is utilized in a variety of applications, including fraud detection, intrusion detection, environmental monitoring, and medical diagnostics, so it's important to spot outliers in data streams. Outlier identification is done in a variety of ways. For outlier detection in data streams, some of them employ the K- Means technique, which aids in the formation of a similar group or cluster of data points. In any application, outlier detection is critical. In this research, we examined various outlier identification algorithms for stream data in depth and provided the results. Keywords—outlier, outlier detection, STORM, k-means, k medoids

Explainable Distance-Based Outlier Detection in Data Streams

Explaining outliers is a topic that attracts a lot of interest; however existing proposals focus on the identification of the relevant dimensions. We extend this rationale for unsupervised distance-based outlier detection, and through investigating subspaces, we propose a novel labeling of outliers in a manner that is intuitive for the user and does not require any training at runtime. Moreover, our solution is applicable to online settings and a complete prototype for detecting and explaining outliers in data streams using massive parallelism has been implemented. Our solution is evaluated in terms of both the quality of the labels derived and the performance.

EMM-CLODS: An Effective Microcluster and Minimal Pruning CLustering-Based Technique for Detecting Outliers in Data Streams

Detecting outliers in data streams is a challenging problem since, in a data stream scenario, scanning the data multiple times is unfeasible, and the incoming streaming data keep evolving. Over the years, a common approach to outlier detection is using clustering-based methods, but these methods have inherent challenges and drawbacks. These include to effectively cluster sparse data points which has to do with the quality of clustering methods, dealing with continuous fast-incoming data streams, high memory and time consumption, and lack of high outlier detection accuracy. This paper aims at proposing an effective clustering-based approach to detect outliers in evolving data streams. We propose a new method called Effective Microcluster and Minimal pruning CLustering-based method for Outlier detection in Data Streams (EMM-CLODS). It is a clustering-based outlier detection approach that detects outliers in evolving data streams by first applying microclustering technique to cluster dense data points and effectively handle objects within a sliding window according to the relevance of their status to their respective neighbors or position. The analysis from our experimental studies on both synthetic and real-world datasets shows that the technique performs well with minimal memory and time consumption when compared to the other baseline algorithms, making it a very promising technique in dealing with outlier detection problems in data streams.

Improved incremental local outlier detection for data streams based on the landmark window model

Improved incremental local outlier detection for data streams based on the landmark window model

Genetic-based Summarization for Local Outlier Detection in Data Stream

Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has two phases. First, the summarization phase that tries to summarize the past data arrived. Second, the detection phase detects the outliers from the new arriving data. The summarization phase uses a genetic algorithm to try to find the subset of points that can represent the whole original set. our experiments have been done over real datasets. Our experiments confirming the effectiveness of the proposed approach and the high quality of approximate solutions in a set of real-world streaming data.

ADINOF: adaptive density summarizing incremental natural outlier detection in data stream

Outlier detection in the stream data has emerged as the challenging problems with the precipitously growing demand of applications like intrusion detection, sensor malfunctioning, fraud detection, and system failures, etc. To address these problems many density-based algorithms have been proposed for detecting the outliers in stream data. Still, it suffers a serious problem with the degree of outlierness measures on its neighbors. Using a right number k is not straightforward in the stream data since we do not know prior distribution of neighbor’s points. And, it must improvise with incoming data as appeared in the stream data. Additionally, stream-based algorithms are not able to detect sequential outliers as well as are having memory constraints. These challenges motivate the authors to propose “Self-Adaptive Density Summarizing incremental Natural Outlier Detection in Data Stream (ADINOF) with skipping scheme and without skipping scheme (ADINOF_NS)” that successfully overcome the challenges. Our comprehensive experimental evaluations demonstrate that ADINOF and ADINOF_NS significantly outperforms the competitive executed algorithms (iLOF, MiLOF, TADILOF, DILOF, and DILOF_NS).

Outlier Detection in Data Streams — A Comparative Study of Selected Methods

Outlier detection is an increasingly important and intensively developing area of research. This paper focuses on the problem of outlier detection in data streams. It presents a performance comparison of selected statistical algorithms: AutoRegressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space Model (ETS), Seasonal Hybrid Extreme Studentized Deviation (SHESD), Non-parametric methodology (NMV), and Chen-Liu method (CHL). Based on four data streams from the Kaggle Repository and DataHub Repository, the study provides results concerning the number of outliers detected by each algorithm and the algorithms’ operation times. The experiments were performed on data streams of different lengths (from a few hundred to 1200 records), characterized by the presence of different types of outliers.

TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams.

Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.

Real-time distance-based outlier detection in data streams

Real-time outlier detection in data streams has drawn much attention recently as many applications need to be able to detect abnormal behaviors as soon as they occur. The arrival and departure of streaming data on edge devices impose new challenges to process the data quickly in real-time due to memory and CPU limitations of these devices. Existing methods are slow and not memory efficient as they mostly focus on quick detection of inliers and pay less attention to expediting neighbor searches for outlier candidates. In this study, we propose a new algorithm, CPOD, to improve the efficiency of outlier detections while reducing its memory requirements. CPOD uses a unique data structure called "core point" with multi-distance indexing to both quickly identify inliers and reduce neighbor search spaces for outlier candidates. We show that with six real-world and one synthetic dataset, CPOD is, on average, 10, 19, and 73 times faster than M_MCOD, NETS, and MCOD, respectively, while consuming low memory.

NETS

This paper addresses the problem of efficiently detecting outliers from a data stream as old data points expire from and new data points enter the window incrementally. The proposed method is based on a newly discovered characteristic of a data stream that the change in the locations of data points in the data space is typically very insignificant. This observation has led to the finding that the existing distance-based outlier detection algorithms perform excessive unnecessary computations that are repetitive and/or canceling out the effects. Thus, in this paper, we propose a novel set-based approach to detecting outliers, whereby data points at similar locations are grouped and the detection of outliers or inliers is handled at the group level. Specifically, a new algorithm NETS is proposed to achieve a remarkable performance improvement by realizing set-based early identification of outliers or inliers and taking advantage of the "net effect" between expired and new data points. Additionally, NETS is capable of achieving the same efficiency even for a high-dimensional data stream through two-level dimensional filtering . Comprehensive experiments using six real-world data streams show 5 to 25 times faster processing time than state-of-the-art algorithms with comparable memory consumption. We assert that NETS opens a new possibility to real-time data stream outlier detection.

An Effective Minimal Probing Approach With Micro-Cluster for Distance-Based Outlier Detection in Data Streams

Outlier detection in data streams is considered a significant task in data mining that targets the discovery of elements in an unprecedented data arrival rate. The fast arrival of data demands fast computation within the shortest period, and with minimal memory usage. Detecting distance-based outliers in such a scenario are more complicated. Existing techniques such as the two best-known methods - Micro-Cluster Outlier Detection (MCOD) and Thresh_LEAP have presented some solutions to these challenges. However, the combination of the strength of both techniques can be a lot more improvement to the individual methods proposed. Therefore, in this paper, we propose a method called Micro-Cluster with Minimal Probing (MCMP), which is a hybrid approach of the combination of the strength of MCOD and Thresh_LEAP. We offer a new distance-based outlier detection technique to minimize the computational cost in detecting distance-based outliers effectively. The proposed MCMP technique is comprised of two approaches. Firstly, we adopt micro-clusters to mitigate the range query search. Then, to deal with the objects outside the micro-clusters, we propose the concept of differentiating between strong and trivial inliers. The proposed method improves the computational speed and memory consumption, while simultaneously maintaining the outlier detection accuracy. Our experiments are conducted on both real-world and synthetic data sets. We varied the window size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(w)$ </tex-math></inline-formula> , neighbor count threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k)$ </tex-math></inline-formula> and distance threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(R)$ </tex-math></inline-formula> , and observed that our method outperforms the state-of-the-art methods in both CPU time and memory consumption in the majority of the datasets.

The Application of a Double CUSUM Algorithm in Industrial Data Stream Anomaly Detection

The effect of the application of machine learning on data streams is influenced by concept drift, drift deviation, and noise interference. This paper proposes a data stream anomaly detection algorithm combined with control chart and sliding window methods. This algorithm is named DCUSUM-DS (Double CUSUM Based on Data Stream), because it uses a dual mean value cumulative sum. The DCUSUM-DS algorithm based on nested sliding windows is proposed to satisfy the concept drift problem; it calculates the average value of the data within the window twice, extracts new features, and then calculates accumulated and controlled graphs to avoid misleading by interference points. The new algorithm is simulated using drilling engineering industrial data. Compared with automatic outlier detection for data streams (A-ODDS) and with sliding nest window chart anomaly detection based on data streams (SNWCAD-DS), the DCUSUM-DS can account for concept drift and shield a small amount of interference deviating from the overall data. Although the algorithm complexity increased from 0.1 second to 0.19 second, the classification accuracy receiver operating characteristic (ROC) increased from 0.89 to 0.95. This meets the needs of the oil drilling industry data stream with a sampling frequency of 1 Hz, and it improves the classification accuracy.

Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection

Since data stream anomaly detection algorithms based on sliding windows are sensitive to the abnormal deviation of individual interference data, this paper presents a sliding nest window chart anomaly detection based on the data stream (SNWCAD-DS) by employing the concept of the sliding window and control chart. By nesting a small sliding window in a large sliding window and analyzing the deviation distance between the small window and the large sliding window, the algorithm increases the out-of-bounds detection ratio and classifies the conceptual drift data stream online. The designed algorithm is simulated on the industrial data stream of drilling engineering. The proposed algorithm SNWCAD is compared with Automatic Outlier Detection for Data Streams (A-ODDS) and Distance-Based Outline Detection for Data Stream (DBOD-DS). The experimental results show that the new algorithm can obtain higher detection accuracy than the compared algorithms. Furthermore, it can shield the influence of individual interference data and satisfy actual engineering needs.

Fast Memory Efficient Local Outlier Detection in Data Streams

Outlier detection is an important task in data mining, with applications ranging from intrusion detection to human gait analysis. With the growing need to analyze high speed data streams, the task ...

Fast Memory Efficient Local Outlier Detection in Data Streams

Outlier detection is an important task in data mining, with applications ranging from intrusion detection to human gait analysis. With the growing need to analyze high speed data streams, the task of outlier detection becomes even more challenging as traditional outlier detection techniques can no longer assume that all the data can be stored for processing. While the well-known Local Outlier Factor (LOF) algorithm has an incremental version, it assumes unbounded memory to keep all previous data points. In this paper, we propose a memory efficient incremental local outlier (MiLOF) detection algorithm for data streams, and a more flexible version (MiLOF_F), both have an accuracy close to Incremental LOF but within a fixed memory bound. Our experimental results show that both proposed approaches have better memory and time complexity than Incremental LOF while having comparable accuracy. In addition, we show that MiLOF_F is robust to changes in the number of data points, the number of underlying clusters and the number of dimensions in the data stream. These results show that MiLOF/MiLOF_F are well suited to application environments with limited memory (e.g., wireless sensor networks), and can be applied to high volume data streams.

A Hybrid Clustering Algorithm for Outlier Detection in Data Streams

A Hybrid Clustering Algorithm for Outlier Detection in Data Streams

Distance-based outlier detection in data streams

Continuous outlier detection in data streams has important applications in fraud detection, network security, and public health. The arrival and departure of data objects in a streaming manner impose new challenges for outlier detection algorithms, especially in time and space efficiency. In the past decade, several studies have been performed to address the problem of distance-based outlier detection in data streams (DODDS), which adopts an unsupervised definition and does not have any distributional assumptions on data values. Our work is motivated by the lack of comparative evaluation among the state-of-the-art algorithms using the same datasets on the same platform. We systematically evaluate the most recent algorithms for DODDS under various stream settings and outlier rates. Our extensive results show that in most settings, the MCOD algorithm offers the superior performance among all the algorithms, including the most recent algorithm Thresh_LEAP.

Survey on Outlier Detection in Data Stream

mining provides a way for finding hidden and useful knowledge from the large amount of data .usually we find any information by finding normal trends or distribution of data .But sometimes rare event or data object may provide information which is very interesting to us .Outlier detection is one of the task of data mining .It finds abnormal data point or sequence hidden in the dataset .Data stream is unbounded sequence of data with explicit or implicit temporal context .Data stream is uncertain and dynamic in nature. Traditional outlier detection techniques for static data which require whole dataset for modelling is not suitable for data stream because whole data stream cannot be stored. Network intrusion detection ,web click stream analysis ,fraud detection ,fault detection in machines ,sensor data analysis are some of the applications of data stream outlier detection .In this paper, we have described several issues in data stream outlier detection and usual approaches or techniques for finding outlier in data stream .