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High Dimensional Fuzzy Outlier Detection
2019
Australian Journal of Intelligent Information Processing Systems
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We propose a novel outlier detection approach, namely high dimensional fuzzy outlier detection (HDFOD), to address the pertinence of outlier results, i.e., to find outliers in high dimensions that lack ...
The experiment evaluations reveal that our fuzzy constraint-based outlier detection is superior to two existing high dimensional algorithms. ...
dataset.
5.1 Outlier detection based on subspace Outlier mining of high-dimensional data has been a major challenge owning to the curse of dimensionality. ...
dblp:journals/ajiips/SharmaT19
fatcat:crd7spxrqzc5vpuh2jqnubfhqi
A LoOP based outlier detection method for high dimensional fuzzy data set
2017
Journal of Intelligent & Fuzzy Systems
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Next, by using the left and right scoring defuzzyfied method, a fuzzy data outlier degree is determined. Finally, the efficiency of the method in outlier detection is shown by numerical examples. ...
Despite the importance of fuzzy data and existence of many powerful methods for determining crisp outliers, there are few approaches for identifying outliers in fuzzy database. ...
In spite of the large variety of methods for outlier detection in Crisp data and the importance of fuzziness real world, there are few methods on discovering fuzzy outliers among a set of fuzzy high dimensional ...
doi:10.3233/jifs-151447
fatcat:xre5qpq5wjcpxdxb7towwdj5qu
An Outlier Fuzzy Detection Method Using Fuzzy Set Theory
2019
IEEE Access
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The experiment evaluations reveal that our fuzzy constraint-based outlier detection is superior to two existing full dimensional algorithms. ...
Moreover, FOD algorithm also improves the accuracy of outlier detection. INDEX TERMS Outlier detection, nearness measure, fuzzy constraint, sparse subspace, genetic algorithm. ...
We are motivated to address subspaces based on local outlier detection in a high-dimensional dataset. ...
doi:10.1109/access.2019.2914605
fatcat:nawtoswa7fdyjfkdzn2ikpozoy
Outlier Detection: A Research and Modified Method using Fuzzy Clustering
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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The purpose of this paper briefly provides a survey on outlier detection and a modified approach to detect outlier using Fuzzy clustering. ...
Outlier detection is studied extensively in data mining and developed for certain application domains, while others are generic in nature. ...
Fast means of outlier detection in high dimensional spaces is studied from C.Pizzuti and F.Angiulli [16] . A pattern based outlier detection is studied from H.Jin and K.Zhang [29] . ...
doi:10.35940/ijitee.c1091.0193s20
fatcat:x5fid7jilncw3kz6eouieay5ve
Outlier Detection Methods for Industrial Applications
[chapter]
2008
Advances in Robotics, Automation and Control
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Standard outlier detection methods fail to detect outliers in industrial data because of the high dimensionality of the data. ...
This makes these techniques infeasible for large dataset with high dimensionality (He et al., 2002) .
Clustering Clustering is a basic method to detect potential outliers. ...
Outlier Detection Methods for Industrial Applications, Advances in Robotics, Automation and Control, Jesus Aramburo and Antonio Ramirez Trevino (Ed.), ISBN: 978-953-7619-16-9, InTech, Available from: http ...
doi:10.5772/5526
fatcat:sptduapyovf5rlnrwd6agob6ka
Hubness in Unsupervised Outlier Detection Techniques for High Dimensional Data –A Survey
2015
International Journal of Computer Applications Technology and Research
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Outlier detection in high dimensional data becomes an emerging technique in today's research in the area of data mining. ...
high dimensional data and role of hubness. ...
Improved K-means technique for outlier detection in high dimensional dataset is explored in [13] . ...
doi:10.7753/ijcatr0411.1004
fatcat:l3lxmvua2je7xpvxnp7k7ni2pa
Search Space Engine Optimize Search Using FCC_STF Algorithm in Fuzzy Co-Clustering
[article]
2014
arXiv
pre-print
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Fuzzy co-clustering can be improved if we handle two main problem first is outlier and second curse of dimensionality .outlier problem can be reduce by implementing page replacement algorithm like FIFO ...
Whereas curse of dimensionality problem can be improved by implementing FCC_STF algorithm for web pages obtain by search engine that reduce the outlier problem first. ...
In this thesis solution for outlier and curse of dimensionality is provided. ...
arXiv:1407.6952v1
fatcat:2pk6kp7gmzcmtmgxrr7firjtva
User Activities Outliers Detection; Integration of Statistical and Computational Intelligence Techniques
2014
Computational intelligence
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In this paper, a hybrid technique for user activities outliers detection is introduced. ...
The hybrid technique consists of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based Systems (FRBS). ...
Identifying outliers and abnormality in a high dimensional matrix is a complex process compared with a low dimensional matrix. ...
doi:10.1111/coin.12045
fatcat:5itxo6cfcfbrljolhcokfzk2de
Fuzzy C-Means in High Dimensional Spaces
2011
International Journal of Fuzzy System Applications
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The paper concludes that FCM can only be applied successfully in high dimensions if the prototypes are initialized very close to the cluster centres. ...
High dimensions have a devastating effect on the FCM algorithm and similar algorithms. One effect is that the prototypes run into the centre of gravity of the entire data set. ...
Aggarwal and Yu (2001) provides an interesting inside in detecting outliers in high dimensional spaces. They find outliers by intelligently choosing projections into lower dimensional spaces. ...
doi:10.4018/ijfsa.2011010101
fatcat:2ut6gzdk4fatjjyjgfjf5uej4u
Robust Principal Component Analysis based on Fuzzy Coded Data
2017
Anadolu University Journal of Science and Technology. A : Applied Sciences and Engineering
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For this reason, if there are outliers in dataset, researchers tend to use alternative methods. Use of fuzzy and robust approaches is the leading choice among these methods. ...
In this study, a new approach to robust fuzzy principal component analysis is proposed. ...
In comparison with CPCA and RPCA based on MCD, the proposed RPCA-FCD approach is more robust in the presence of outliers, for the analysis of both low dimensional and high dimensional dataset. ...
doi:10.18038/aubtda.317765
fatcat:7iyrpimtnnbpjoi5honyevtocu
Simplified outlier detection for improving the robustness of a fuzzy model
2019
Science China Information Sciences
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In this way, the multidimensional outlier detection problem can be transformed into a one-dimensional outlier detection problem. ...
We developed a method to simplify the outlier detection with WM method in order to establish a fuzzy model with high robustness in a simple way. ...
doi:10.1007/s11432-018-9545-8
fatcat:ymqhiwquyzcjhei3wsoafghdnu
The Effect of Noise and Outliers on Fuzzy Clustering of High Dimensional Data
2016
Proceedings of the 8th International Joint Conference on Computational Intelligence
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In this work, we analyze the effect of different kinds of noise and outliers on fuzzy clustering algorithms that can handle high dimensional data: FCM with attribute weighting, the multivariate fuzzy c-means ...
Additionally, we propose a new version of PMFCM to enhance its ability handling noise and outliers in high dimensional data. ...
The main objective of this work is to analyze the effect of noise and outliers on fuzzy clustering algorithms for high dimensional data. ...
doi:10.5220/0006070601010108
dblp:conf/ijcci/Himmelspach016
fatcat:q6j7rglznzg6dawxj6vncbmuse
A Review of Outlier Prediction Techniques in Data Mining
2015
Research Journal of Applied Sciences Engineering and Technology
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It plays a vital role to choose, explore and model high dimensional data. ...
The outlier or noise available in the clustered data is accurately removed and retrieves an efficient high dimensional data. ...
However, this approach has high computational complexity due to high-dimensional data sets. ...
doi:10.19026/rjaset.10.1869
fatcat:cjvlirdvvfhcrf36m6n6xijbkq
False Alarms Rate Reduction Using Filtered Monitoring Indices
2017
Algerian journal of signals and systems
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The results, in which the fuzzy logic based filter showed a satisfactory performance, are presented and discussed. ...
The filters that were used are: Standard Median Filter (SMF), Improved Median Filter (IMF) and fuzzy logic based filter. ...
If the r th sample in the window is detected to be an outlier or a noise, the sample z r will be modified as : = =1 =0
FUZZY LOGIC BASED FILTER Fuzzy logic is a branch of fuzzy set theory that attempts ...
doi:10.51485/ajss.v2i1.31
fatcat:6jbyqom3jfdy3hrdsgfj3ejq2i
A Robust Fuzzy Clustering Approach And Its Application To Principal Component Analysis
2010
Intelligent Automation and Soft Computing
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A robust fuzzy clustering approach is proposed to simplify the task of principal component analysis (PCA) by reducing the data complexity of an image. ...
c-means (RFCM) to mitigate the influence of noise and outlier data so that a good result of principal components can be found. ...
All these methods require some knowledge of identifying noise and outliers, which, however, is difficult to be realized in real world applications with high-dimensional observations. ...
doi:10.1080/10798587.2010.10643059
fatcat:rm7rlvxnprfsbh5jcreuimcaoi
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