Fuzzy Clustering Data Arrays with Omitted Observations

Hu, Zhengbing; Bodyanskiy, Yevgeniy; Tyshchenko, Oleksii K.; Tkachov, Vitalii

doi:10.5815/ijisa.2017.06.03

Cited by 25 publications

(6 citation statements)

References 29 publications

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“…3) Model based methods: Using mathematical models to predict the missing values. For example Zhengbing Hu et al [32] tried to recover missing values using methods based on Computaional intelligence.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Comparison of Missing Value Imputation Techniques on APS Failure Prediction

Rafsunjani¹,

Safa²,

Imran³

et al. 2019

IJITCS

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The Air Pressure System (APS) is a type of function used in heavy vehicles to assist braking and gear changing. The APS failure dataset consists of the daily operational sensor data from failed Scania trucks. The dataset is crucial to the manufacturer as it allows to isolate components which caused the failure. However, missing values and imbalanced class problems are the two most challenging limitations of this dataset to predict the cause of the failure. The prediction results can be affected by the way of handling these missing values and imbalanced class problem. In this paper, we have examined and presented the impact of five different missing value imputation techniques namely: Expectation Maximization, Mean Imputation, Soft Impute, MICE, and Iterative SVD in producing significantly better results. We have also performed an empirical comparison of their performance by applying five different classifiers namely: Naive Bayes, KNN, SVM, Random Forest, and Gradient Boosted Tree on this highly imbalanced dataset. The primary aim of this study is to observe the impact of the mentioned missing value imputation techniques in the enhancement of the prediction results, performing an empirical comparison to figure out the best classification model and imputation technique. We found that the MICE imputation and the random under-sampling techniques are the highest influential techniques for improving the prediction performance and false negative rate.

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Section: Related Workmentioning

confidence: 99%

An Empirical Comparison of Missing Value Imputation Techniques on APS Failure Prediction

Rafsunjani¹,

Safa²,

Imran³

et al. 2019

IJITCS

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“…Possibilistic approach to clustering is proposed in [2]. Medoid based clustering algorithms were proposed in [21,22]. Hybrid clustering approach is dealt in [3].…”

Section: Effectiveness In Capturing the Sequential Relations Between mentioning

confidence: 99%

Fuzzy Clustering of Sequential Data

Tripathy¹,

Vit²,

Rahul³

2019

IJISA

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With the increase in popularity of the Internet and the advancement of technology in the fields like bioinformatics and other scientific communities the amount of sequential data is on the increase at a tremendous rate. With this increase, it has become inevitable to mine useful information from this vast amount of data. The mined information can be used in various spheres; from day to day web activities like the prediction of next web pages, serving better advertisements, to biological areas like genomic data analysis etc. A rough set based clustering of sequential data was proposed by Kumar et al recently. They defined and used a measure, called Sequence and Set Similarity Measure to determine similarity in data. However, we have observed that this measure does not reflect some important characteristics of sequential data. As a result, in this paper, we used the fuzzy set technique to introduce a similarity measure, which we termed as Kernel and Set Similarity Measure to find the similarity of sequential data and generate overlapping clusters. For this purpose, we used exponential string kernels and Jaccard's similarity index. The new similarity measure takes an account of the order of items in the sequence as well as the content of the sequential pattern. In order to compare our algorithm with that of Kumar et al, we used the MSNBC data set from the UCI repository, which was also used in their paper. As far as our knowledge goes, this is the first fuzzy clustering algorithm for sequential data.

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“…A fuzzy set is represented as (X, μ) where X is a set and μ: X → [0, 1] is a membership function [36], [37]. For every x ϵ X, the value μ(x) is known as the grade of membership of x.…”

Section: B Fuzzy Control System (Fcs)mentioning

confidence: 99%

Fuzzy-based User Behavior Characterization to Detect HTTP-GET Flood Attacks

Singh¹,

Singh²,

Kumar³

2018

IJISA

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Internet was designed to serve the basic requirement of data transfer between systems. The security perspectives were therefore overlooked due to which the Internet remains vulnerable to a variety of attacks. Among all the possible attacks, Distributed Denial of Service (DDoS) attack is one of the eminent threats that target the availability of the online services to the intended clients. Now-a-days, attackers target application layer of the network stack to orchestrate attacks having a high degree of sophistication. GET flood attacks have been very much prevalent in recent years primarily due to advancement of bots allowing impersonating legitimate client behavior. Differentiating between a human client and a bot is therefore necessary to mitigate an attack. This paper introduces a mitigation framework based on Fuzzy Control System that takes as input two novel detection parameters. These detection parameters make use of clients' behavioral characteristic to measure their respective legitimacy. We design an experimental setup that incorporates two widely used benchmark web logs (Clarknet and WorldCup) to build legitimate and attack datasets. Further, we use these datasets to assess the performance of the proposed through well-known evaluation metrics. The results obtained during this work point towards the efficiency of our proposed system to mitigate a wide range of GET flood attack types.

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Fuzzy Clustering Data Arrays with Omitted Observations

Cited by 25 publications

References 29 publications

An Empirical Comparison of Missing Value Imputation Techniques on APS Failure Prediction

An Empirical Comparison of Missing Value Imputation Techniques on APS Failure Prediction

Fuzzy Clustering of Sequential Data

Fuzzy-based User Behavior Characterization to Detect HTTP-GET Flood Attacks

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