Detecting distance-based outliers in streams of data

Angiulli, Fabrizio; Fassetti, Fabio

doi:10.1145/1321440.1321552

Cited by 186 publications

(132 citation statements)

References 23 publications

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“…Assumption based method can work quite well if prior assumption made about data is correct. Finally, for further reading, we direct the reader to a recent book on outlier analysis [1] and [7] for a tutorial version of this survey.…”

Section: Resultsmentioning

confidence: 99%

A Survey paper on An Effective Analytical Approaches for Detecting Outlier in Continuous Time Variant Data Stream

Purankar¹

2016

IJECS

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Outlier detection and is an important branch of data mining. Data mining is extensively studied field of research area; where most of the work is focused on the information discovery. A data stream is a massive sequence of data objects continuously generated at much faster rate. There are various approaches and methods are used for outlier detection. Some of them use K-Means algorithm for outlier detection in data streams which help to create a similar group or cluster of data points. The K-means algorithm is the best known partitioned clustering algorithm. As we know that streaming data often fails to scan the multiple items and also the new concepts may keep evolving in coming data over time hence the outlier detection plays the challenging role in the streaming data. The irrelevant attributes can be termed as noisy attribute at the time of working with the data streams objects and such attributes imposes the challenge. In high dimensional data the number of attributes associated with the dataset is very large and it makes the dataset unmanageable. Clustering is a data stream mining task which is very useful to gain insight of data and data characteristics. Clustering is also used as a pre-processing step in over all mining process for an example clustering is used for outlier detection and for building and development of Hybrid approach. Purpose of this paper is to review of Hybrid approach of outlier detection with others approach which uses K-Means algorithm for clustering dataset with some other techniques like Euclidean distance approach. Various application domains of outlier detection are discussed in this paper.

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Section: Resultsmentioning

confidence: 99%

A Survey paper on An Effective Analytical Approaches for Detecting Outlier in Continuous Time Variant Data Stream

Purankar¹

2016

IJECS

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“…To use such method in streaming data, time sliding window for constant removal of old outdated samples, need to be introduced. The main work related to this subject makes use of ISB structure for storing neighborhood relation [7] or Yang algorithms which utilize some properties of sliding window [8].…”

Section: Available Algorithmsmentioning

confidence: 99%

Anomaly detection in server metrics with use of one-sided median algorithm

Zacher¹,

Ryba

2017

Journal of Applied Computer Science Methods

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In this paper we consider the problem of anomaly detection over time series metrics data took from one of corporate grade mail service cluster. We propose the algorithm based on one-sided median concept and present some results of experiments showing impact of parameters settings on algorithm performance. In addition we present short description of classes of anomalies discovered in monitored system. Proposed one-sided median based algorithm shows great robustness and good detection rate and can be considered as possible simple production ready solution.

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“…The outlier is detected without any interference in the clustering process. Various clustering approaches are used for the outlier detection [11]. Clustering on streaming data is categorized by grid based and k means/k median methods [36].…”

Section: Cluster Based Outlier Detectionmentioning

confidence: 99%

Comparative Study of Outlier Detection Algorithms

Kaur¹,

Garg²

2016

IJCA

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As the dimension of the data is increasing day by day, outlier detection is emerging as one of the active area of research. Finding of the outliers from large data sets is the main problem. Outlier is considered as the pattern that is different from the rest of the patterns present in the data set. The detection of the outlier in the data set is an important process as it helps in acquiring the useful information that further helps in the data analysis. Various algorithms have been proposed till date for the detection of the outliers. This paper covers a study of various outlier detection algorithms like Statistical based outlier detection, Depth based outlier detection, Clustering based technique, Density based outlier detection etc. Comparison study of these outlier detection methods is done to find out which of the outlier detection algorithms are more applicable on high dimensional data.

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Detecting distance-based outliers in streams of data

Cited by 186 publications

References 23 publications

A Survey paper on An Effective Analytical Approaches for Detecting Outlier in Continuous Time Variant Data Stream

A Survey paper on An Effective Analytical Approaches for Detecting Outlier in Continuous Time Variant Data Stream

Anomaly detection in server metrics with use of one-sided median algorithm

Comparative Study of Outlier Detection Algorithms

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