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High Dimensional Fuzzy Outlier Detection

Vasudev Sharma, B. K. Tripathy
2019 Australian Journal of Intelligent Information Processing Systems  
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

Alireza Fakharzadeh Jahromi, Fateme Zarei
2017 Journal of Intelligent & Fuzzy Systems  
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

Lizhong Jin, Junjie Chen, Xiaobo Zhang
2019 IEEE Access  
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  
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]

Silvia Cateni, Valentina Colla, Marco Vannucci
2008 Advances in Robotics, Automation and Control  
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

R.Lakshmi Devi, R. Amalraj
2015 International Journal of Computer Applications Technology and Research  
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]

Monika Rani, Anubha Parashar, Jyoti Chaturvedi, Anu Malviya
2014 arXiv   pre-print
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

Sawsan Mahmoud, Ahmad Lotfi, Caroline Langensiepen
2014 Computational intelligence  
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

Roland Winkler, Frank Klawonn, Rudolf Kruse
2011 International Journal of Fuzzy System Applications  
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

Baris Alkan, Sevgi GANIK
2017 Anadolu University Journal of Science and Technology. A : Applied Sciences and Engineering  
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

Yali Jin, Weihua Cao, Min Wu, Yan Yuan
2019 Science China Information Sciences  
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

Ludmila Himmelspach, Stefan Conrad
2016 Proceedings of the 8th International Joint Conference on Computational Intelligence  
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

S. Kannan, K. Somasundaram
2015 Research Journal of Applied Sciences Engineering and Technology  
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

M. AMMICHE, A. KOUADRI
2017 Algerian journal of signals and systems  
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

Ying-Kuei Yang, Chien-Nan Lee, Horng-Lin Shieh
2010 Intelligent Automation and Soft Computing  
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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