Fuzzy logic can be used to reason like humans and can deal with uncertainty other than randomness. Outlier detection is a difficult task to be performed, due to uncertainty involved in it. The outlier itself is a fuzzy concept and difficult to determine in a deterministic way. fuzzy logic system is very promising, since they exactly tackle the situation associated with outliers. Fuzzy logic that addresses the seemingly conflicting goals (i) removing noise, (ii) smoothing out outliers and certain other salient feature. This paper provides a detailed fuzzy logic used for outlier detection by discussing their pros and cons. Thus this is a very helpful document for naive researchers in this field.
Comparison of fuzzy neural clustering based outlier detection techniquesIAEME Publication
The document compares fuzzy-neural clustering based outlier detection techniques. It discusses how fuzzy logic can handle uncertainty and neural networks can learn and adapt. It provides an overview of fuzzy clustering based outlier detection techniques like fuzzy c-means clustering, which allows data points to belong to multiple clusters to varying degrees. It also discusses neural network based outlier detection. The document aims to compare outlier detection techniques involving fuzzy and/or neural approaches based on clustering, focusing on their strengths and weaknesses.
Hybrid Approach for Brain Tumour Detection in Image Segmentationijtsrd
In this paper we have considered illustrating a few techniques. But the numbers of techniques are so large they cannot be all addressed. Image segmentation forms the basics of pattern recognition and scene analysis problems. The segmentation techniques are numerous in number but the choice of one technique over the other depends only on the application or requirements of the problem that is being considered. Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. The main idea of this is to present facts about brain tumour detection system and various data mining methods used in this system. This is focuses on scalable data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data power to perform complex transformations and analysis will be measured between the way of measuring the Furrier and Wavelet Transform distance. Sandeep | Jyoti Kataria "Hybrid Approach for Brain Tumour Detection in Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33409.pdf Paper Url: https://www.ijtsrd.com/medicine/other/33409/hybrid-approach-for-brain-tumour-detection-in-image-segmentation/sandeep
Analysis on different Data mining Techniques and algorithms used in IOTIJERA Editor
In this paper, we discusses about five functionalities of data mining in IOT that affects the performance and that
are: Data anomaly detection, Data clustering, Data classification, feature selection, time series prediction. Some
important algorithm has also been reviewed here of each functionalities that show advantages and limitations as
well as some new algorithm that are in research direction. Here we had represent knowledge view of data
mining in IOT.
KNOWLEDGE BASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST CANCERcscpconf
In this paper, we study the performance criterion of machine learning tools in classifying breast cancer. We compare the data mining tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART. We used both binary and multi class data sets namely WBC, WDBC and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to accuracy, precision, sensitivity and specificity in detecting breast cancer
Geometric Correction for Braille Document Images csandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
IRJET - Survey on Clustering based Categorical Data ProtectionIRJET Journal
This document summarizes and compares various techniques for clustering and protecting categorical data to preserve privacy. It discusses subtractive clustering, robust hierarchical clustering, decision tree clustering, outlier detection methods like statistical, depth-based, distance-based and density-based techniques. It also covers protection algorithms such as L-diversity and an evolutionary optimization approach. Tables are provided to compare the benefits and drawbacks of different clustering, outlier detection and protection algorithms for categorical data privacy. The document concludes that clustering is useful for categorical data privacy but challenges remain in improving utility and security.
Fault detection of imbalanced data using incremental clusteringIRJET Journal
This document proposes a method for fault detection in imbalanced data using incremental clustering with feature selection. Standard classification algorithms are not suitable for fault detection in imbalanced data as they prioritize the majority class. The proposed method uses incremental clustering to detect faults, maintaining statistical summaries for each cluster. It selects features using a minimum spanning tree-based algorithm to reduce dimensionality and improve efficiency. This feature selection aims to choose a subset of strongly related features while removing irrelevant and redundant features. The selected features are then used as input for the incremental clustering fault detection method to achieve better classification accuracy and result quality for imbalanced fault detection problems.
COMPARATIVE ANALYSIS OF MINUTIAE BASED FINGERPRINT MATCHING ALGORITHMSijcsit
Biometric matching involves finding similarity between fingerprint images.The accuracy and speed of the
matching algorithmdetermines its effectives. This researchaims at comparing two types of matching
algorithms namely(a) matching using global orientation features and (b) matching using minutia triangulation.The comparison is done using accuracy, time and number of similar features. The experiment is conducted on a datasets of 100 candidates using four (4) fingerprints from each candidate. The data is sampled from a mass registration conducted by a reputable organization in Kenya.Theresearch reveals that fingerprint matching based on algorithm (b) performs better in speed with an average of 38.32 milliseconds
as compared to matching based on algorithm (a) with an average of 563.76 milliseconds. On accuracy,algorithm(a) performs better with an average accuracy of 0.142433 as compared to algorithm (b) with an average accuracy score of 0.004202.
Comparison of fuzzy neural clustering based outlier detection techniquesIAEME Publication
The document compares fuzzy-neural clustering based outlier detection techniques. It discusses how fuzzy logic can handle uncertainty and neural networks can learn and adapt. It provides an overview of fuzzy clustering based outlier detection techniques like fuzzy c-means clustering, which allows data points to belong to multiple clusters to varying degrees. It also discusses neural network based outlier detection. The document aims to compare outlier detection techniques involving fuzzy and/or neural approaches based on clustering, focusing on their strengths and weaknesses.
Hybrid Approach for Brain Tumour Detection in Image Segmentationijtsrd
In this paper we have considered illustrating a few techniques. But the numbers of techniques are so large they cannot be all addressed. Image segmentation forms the basics of pattern recognition and scene analysis problems. The segmentation techniques are numerous in number but the choice of one technique over the other depends only on the application or requirements of the problem that is being considered. Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. The main idea of this is to present facts about brain tumour detection system and various data mining methods used in this system. This is focuses on scalable data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data power to perform complex transformations and analysis will be measured between the way of measuring the Furrier and Wavelet Transform distance. Sandeep | Jyoti Kataria "Hybrid Approach for Brain Tumour Detection in Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33409.pdf Paper Url: https://www.ijtsrd.com/medicine/other/33409/hybrid-approach-for-brain-tumour-detection-in-image-segmentation/sandeep
Analysis on different Data mining Techniques and algorithms used in IOTIJERA Editor
In this paper, we discusses about five functionalities of data mining in IOT that affects the performance and that
are: Data anomaly detection, Data clustering, Data classification, feature selection, time series prediction. Some
important algorithm has also been reviewed here of each functionalities that show advantages and limitations as
well as some new algorithm that are in research direction. Here we had represent knowledge view of data
mining in IOT.
KNOWLEDGE BASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST CANCERcscpconf
In this paper, we study the performance criterion of machine learning tools in classifying breast cancer. We compare the data mining tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART. We used both binary and multi class data sets namely WBC, WDBC and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to accuracy, precision, sensitivity and specificity in detecting breast cancer
Geometric Correction for Braille Document Images csandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
IRJET - Survey on Clustering based Categorical Data ProtectionIRJET Journal
This document summarizes and compares various techniques for clustering and protecting categorical data to preserve privacy. It discusses subtractive clustering, robust hierarchical clustering, decision tree clustering, outlier detection methods like statistical, depth-based, distance-based and density-based techniques. It also covers protection algorithms such as L-diversity and an evolutionary optimization approach. Tables are provided to compare the benefits and drawbacks of different clustering, outlier detection and protection algorithms for categorical data privacy. The document concludes that clustering is useful for categorical data privacy but challenges remain in improving utility and security.
Fault detection of imbalanced data using incremental clusteringIRJET Journal
This document proposes a method for fault detection in imbalanced data using incremental clustering with feature selection. Standard classification algorithms are not suitable for fault detection in imbalanced data as they prioritize the majority class. The proposed method uses incremental clustering to detect faults, maintaining statistical summaries for each cluster. It selects features using a minimum spanning tree-based algorithm to reduce dimensionality and improve efficiency. This feature selection aims to choose a subset of strongly related features while removing irrelevant and redundant features. The selected features are then used as input for the incremental clustering fault detection method to achieve better classification accuracy and result quality for imbalanced fault detection problems.
COMPARATIVE ANALYSIS OF MINUTIAE BASED FINGERPRINT MATCHING ALGORITHMSijcsit
Biometric matching involves finding similarity between fingerprint images.The accuracy and speed of the
matching algorithmdetermines its effectives. This researchaims at comparing two types of matching
algorithms namely(a) matching using global orientation features and (b) matching using minutia triangulation.The comparison is done using accuracy, time and number of similar features. The experiment is conducted on a datasets of 100 candidates using four (4) fingerprints from each candidate. The data is sampled from a mass registration conducted by a reputable organization in Kenya.Theresearch reveals that fingerprint matching based on algorithm (b) performs better in speed with an average of 38.32 milliseconds
as compared to matching based on algorithm (a) with an average of 563.76 milliseconds. On accuracy,algorithm(a) performs better with an average accuracy of 0.142433 as compared to algorithm (b) with an average accuracy score of 0.004202.
IRJET- Classification of Crops and Analyzing the Acreages of the FieldIRJET Journal
This document summarizes different methods for classifying crops using satellite images. It discusses supervised classification techniques like maximum likelihood classification which uses statistical modeling of training sites to classify pixels. Unsupervised classification uses cluster analysis to group pixels into natural spectral classes without prior class definitions. Specific classification algorithms discussed include ISODATA, maximum likelihood, parallelepiped, minimum distance to means. The document aims to help farmers, governments and industries analyze crop growth and yields using remote sensing techniques.
Fuzzy k c-means clustering algorithm for medical imageAlexander Decker
This document summarizes and compares several algorithms used for medical image segmentation, including thresholding, classifiers, Markov random field models, artificial neural networks, atlas-guided approaches, deformable models, and clustering analysis methods like k-means and fuzzy c-means. It provides details on the fuzzy c-means and k-means clustering algorithms, including their process and flowcharts. A new fuzzy k-c-means algorithm is proposed that combines fuzzy c-means and k-means clustering to improve segmentation time. The algorithms are tested on MRI brain images and their results are analyzed and compared based on time, iterations, and accuracy.
The document summarizes a novel approach for privacy preserving data mining on continuous and discrete data sets. The approach converts original sample data sets into a group of "unreal" data sets from which the original samples cannot be reconstructed. However, an accurate decision tree can still be built directly from the unreal data sets. This protects privacy while maintaining data mining utility. The approach determines information entropy and generates a decision tree using the unreal data sets and a perturbing set, without reconstructing the original samples.
C LUSTERING B ASED A TTRIBUTE S UBSET S ELECTION U SING F AST A LGORITHmIJCI JOURNAL
In machine learning and data mining, attribute sel
ect is the practice of selecting a subset o
f most
consequential attributes for utilize in model const
ruction. Using an attribute select method is that t
he data
encloses many redundant or extraneous attributes. W
here redundant attributes are those which sup
ply
no supplemental information than the presently
selected attributes, and impertinent attribut
es offer
no valuable information in any context
COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC APPROACHES FOR CRACK...ijaia
This paper proposes two algorithms of crack detection one using fuzzy logic (FL) and the other artificial neural networks (ANN). Since modal parameters are very sensitive to damages, the first three relative natural frequencies are used as three inputs and the corresponding relative crack location, relative crack depth are used as the two outputs in the algorithms. The three natural frequencies for an undamaged beam and different cases of damaged beam (Single crack at various locations with varying depths) were obtained by modelling and simulating the beams using a finite element based (FEM) software. Results concluded that both the approaches can be successfully employed in crack detection in a beam like structure but FL approach performed better in determining relative crack depth whereas ANN approach performed better in determining relative crack location. All the comparisons made in the study are based on the R2 values.
Iaetsd an efficient and large data base using subset selection algorithmIaetsd Iaetsd
The document presents a new feature selection algorithm called FAST (Feature Cluster-based Subset Selection) that aims to efficiently reduce dimensionality by removing irrelevant and redundant features. The FAST algorithm works in two steps: (1) it clusters features using graph theoretic methods, and (2) it selects the most representative feature from each cluster. This clustering-based approach has a high probability of selecting useful and independent features. The algorithm is evaluated on high dimensional datasets and shown to improve learning accuracy while reducing dimensionality compared to other feature selection methods.
Performance Evaluation of Different Data Mining Classification Algorithm and ...IOSR Journals
This document evaluates the performance of different data mining classification algorithms and predictive analysis. It applies algorithms like decision trees, naive Bayes, k-nearest neighbor, neural networks, and support vector machines to datasets like Iris, liver disorder, and E. coli. The results show that neural networks and k-nearest neighbor achieved the best performance on these datasets, with accuracy rates up to 97.33% for Iris classification. Feature selection techniques like removing zero-weighted attributes were also found to improve some algorithm performance. Predictive analysis experiments found that neural networks and k-nearest neighbor were most accurate at predicting new class labels.
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges involved. It then reviews various existing approaches for clustering uncertain data, including using soft classifiers and probabilistic databases. The document proposes combining k-means clustering with Voronoi diagrams and indexing techniques to improve the performance and efficiency of clustering uncertain datasets. It outlines a plan to integrate k-means with Voronoi diagrams and indexing to reduce execution time and increase clustering performance and results for uncertain data. Finally, it concludes that combining clustering with indexing approaches can better handle uncertain data clustering challenges.
Tracking of Fluorescent Cells Based on the Wavelet Otsu Modelrahulmonikasharma
The mainstay of the project is to demonstrate that the proposed tracking scheme is more accurate and significantly faster than the other state-of-the-art tracking by model evolution approaches.The model is validated by comparing it to the original algorithm.The proposed tracking scheme involves two steps. First, coherence-enhancing diffusion filtering is applied on each frame to reduce the amount of noise and enhance flow-like structures. Second, the image segmentation is done by the Wavelet OTSU method in the fast level set-like and graph cut frameworks. This model evolution approach has also been extended to deal with many cells concurrently. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of mouse carcinoma cells.
Segmentation of unhealthy region of plant leaf using image processing techniq...eSAT Publishing House
1. This document discusses various image segmentation techniques that can be used to segment diseased regions of plant leaves for disease identification.
2. Common segmentation techniques discussed include K-means clustering, Fuzzy C-means clustering, Penalized Fuzzy C-means, and unsupervised segmentation.
3. After segmentation, texture and color features are extracted from the diseased regions to identify the plant disease using classification methods. The choice of segmentation technique depends on factors like noise levels and boundary definitions in the image.
Data mining is an integrated field, depicted technologies in combination to the areas having database, learning by machine, statistical study, and recognition in patterns of same type, information regeneration, A.I networks, knowledge-based portfolios, artificial intelligence, neural network, and data determination. In real terms, mining of data is the investigation of provisional data sets for finding hidden connections and to gather the information in peculiar form which are justifiable and understandable to the owner of gather or mined data. An unsupervised formula which differentiate data components into collections by which the components in similar group are more allied to one other and items in rest of cluster seems to be non-allied, by the criteria of measurement of equality or predictability is called process of clustering. Cluster analysis is a relegating task that is utilized to identify same group of object and it is additionally one of the most widely used method for many practical application in data mining. It is a method of grouping objects, where objects can be physical, such as a student or may be a summary such as customer comportment, handwriting. It has been proposed many clustering algorithms that it falls into the different clustering methods. The intention of this paper is to provide a relegation of some prominent clustering algorithms.
A Neural Network Approach to Identify Hyperspectral Image Content IJECEIAES
A Hyperspectral is the imaging technique that contains very large dimension data with the hundreds of channels. Meanwhile, the Hyperspectral Images (HISs) delivers the complete knowledge of imaging; therefore applying a classification algorithm is very important tool for practical uses. The HSIs are always having a large number of correlated and redundant feature, which causes the decrement in the classification accuracy; moreover, the features redundancy come up with some extra burden of computation that without adding any beneficial information to the classification accuracy. In this study, an unsupervised based Band Selection Algorithm (BSA) is considered with the Linear Projection (LP) that depends upon the metric-band similarities. Afterwards Monogenetic Binary Feature (MBF) has consider to perform the „texture analysis‟ of the HSI, where three operational component represents the monogenetic signal such as; phase, amplitude and orientation. In post processing classification stage, feature-mapping function can provide important information, which help to adopt the Kernel based Neural Network (KNN) to optimize the generalization ability. However, an alternative method of multiclass application can be adopt through KNN, if we consider the multi-output nodes instead of taking single-output node.
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATAIJSCAI Journal
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
I gave this talk in the EDBT 2014 conference, which tool place in Athens, Greece.
I show how data examples can be used to characterize the behavior of scientific modules. I present a new methods that automatically generate the data examples, and show that such data examples are useful for the human user to understand the task of the modules, and that they can be used to assist curators in repairing broken workflows (i.e., workflows for which one or more modules are no longer supplied by their providers)
This document describes a study that aims to predict the results of visual field perimetry tests for glaucoma detection using data from optical coherence tomography (OCT) scans. The study involves using optical character recognition to extract data from OCT scan reports, pre-processing the data, and then applying regression techniques from data mining to identify patterns in the data that could be used to predict perimetry test results. Cross-validation is used to assess the accuracy of the predictive model. The document provides background on glaucoma and discusses other existing techniques for glaucoma detection such as confocal scanning laser ophthalmology and scanning laser polarimetry.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
This document compares three classification methods - artificial neural networks, decision trees, and logistic regression - for predicting malignancy in thyroid tumor patients using a clinical dataset. It describes each method and applies them to a dataset of 259 thyroid tumor patients. The artificial neural network achieved 98% accuracy on the training set and 92% on the validation set. The decision tree method used 150 cases to build a model and achieved 86% accuracy. Logistic regression analysis resulted in 88% accuracy. The artificial neural network was able to accurately predict malignancy and identified important attributes like multiple nodules and family cancer history.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Outlier Detection in Data Mining An Essential Component of Semiconductor Manu...yieldWerx Semiconductor
Outlier detection is a critical research field within data mining due to its vast range of applications including fraud detection, cybersecurity, health diagnostics, and significantly for the semiconductor manufacturing industry. It refers to identifying data points that significantly deviate from expected patterns, providing crucial insights into different aspects of data. However, the ambiguity between outliers and normal behavior, evolving definitions of 'normal', application-specific techniques, and noisy data mimicking outliers, often complicate the outlier detection process. This review article offers an in-depth analysis of the most advanced outlier detection methods, presenting a thorough understanding of future research prospects.
A Survey on Cluster Based Outlier Detection Techniques in Data StreamIIRindia
In recent days, Data Mining (DM) is an emerging area of computational intelligence that provides new techniques, algorithms and tools for processing large volumes of data. Clustering is the most popular data mining technique today. Clustering used to separate a dataset into groups that finds intra-group similarity and inter-group similarity. Outlier detection (Anomaly) is to find small groups of data objects that are different when compared with rest of data. The outlier detection is an essential part of mining in data stream. Data Stream (DS) used to mine continuous arrival of high speed data Items. It plays an important role in the fields of telecommunication services, E-Commerce, Tracking customer behaviors and Medical analysis. Detecting outliers over data stream is an active research area. This survey presents the overview of fundamental outlier detection approaches and various types of outlier detection methods in data stream.
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .This combination not only
provides the great boost to the detection of outliers but also enhances its accuracy and precision.
A MIXTURE MODEL OF HUBNESS AND PCA FOR DETECTION OF PROJECTED OUTLIERSZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .
IRJET- Classification of Crops and Analyzing the Acreages of the FieldIRJET Journal
This document summarizes different methods for classifying crops using satellite images. It discusses supervised classification techniques like maximum likelihood classification which uses statistical modeling of training sites to classify pixels. Unsupervised classification uses cluster analysis to group pixels into natural spectral classes without prior class definitions. Specific classification algorithms discussed include ISODATA, maximum likelihood, parallelepiped, minimum distance to means. The document aims to help farmers, governments and industries analyze crop growth and yields using remote sensing techniques.
Fuzzy k c-means clustering algorithm for medical imageAlexander Decker
This document summarizes and compares several algorithms used for medical image segmentation, including thresholding, classifiers, Markov random field models, artificial neural networks, atlas-guided approaches, deformable models, and clustering analysis methods like k-means and fuzzy c-means. It provides details on the fuzzy c-means and k-means clustering algorithms, including their process and flowcharts. A new fuzzy k-c-means algorithm is proposed that combines fuzzy c-means and k-means clustering to improve segmentation time. The algorithms are tested on MRI brain images and their results are analyzed and compared based on time, iterations, and accuracy.
The document summarizes a novel approach for privacy preserving data mining on continuous and discrete data sets. The approach converts original sample data sets into a group of "unreal" data sets from which the original samples cannot be reconstructed. However, an accurate decision tree can still be built directly from the unreal data sets. This protects privacy while maintaining data mining utility. The approach determines information entropy and generates a decision tree using the unreal data sets and a perturbing set, without reconstructing the original samples.
C LUSTERING B ASED A TTRIBUTE S UBSET S ELECTION U SING F AST A LGORITHmIJCI JOURNAL
In machine learning and data mining, attribute sel
ect is the practice of selecting a subset o
f most
consequential attributes for utilize in model const
ruction. Using an attribute select method is that t
he data
encloses many redundant or extraneous attributes. W
here redundant attributes are those which sup
ply
no supplemental information than the presently
selected attributes, and impertinent attribut
es offer
no valuable information in any context
COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC APPROACHES FOR CRACK...ijaia
This paper proposes two algorithms of crack detection one using fuzzy logic (FL) and the other artificial neural networks (ANN). Since modal parameters are very sensitive to damages, the first three relative natural frequencies are used as three inputs and the corresponding relative crack location, relative crack depth are used as the two outputs in the algorithms. The three natural frequencies for an undamaged beam and different cases of damaged beam (Single crack at various locations with varying depths) were obtained by modelling and simulating the beams using a finite element based (FEM) software. Results concluded that both the approaches can be successfully employed in crack detection in a beam like structure but FL approach performed better in determining relative crack depth whereas ANN approach performed better in determining relative crack location. All the comparisons made in the study are based on the R2 values.
Iaetsd an efficient and large data base using subset selection algorithmIaetsd Iaetsd
The document presents a new feature selection algorithm called FAST (Feature Cluster-based Subset Selection) that aims to efficiently reduce dimensionality by removing irrelevant and redundant features. The FAST algorithm works in two steps: (1) it clusters features using graph theoretic methods, and (2) it selects the most representative feature from each cluster. This clustering-based approach has a high probability of selecting useful and independent features. The algorithm is evaluated on high dimensional datasets and shown to improve learning accuracy while reducing dimensionality compared to other feature selection methods.
Performance Evaluation of Different Data Mining Classification Algorithm and ...IOSR Journals
This document evaluates the performance of different data mining classification algorithms and predictive analysis. It applies algorithms like decision trees, naive Bayes, k-nearest neighbor, neural networks, and support vector machines to datasets like Iris, liver disorder, and E. coli. The results show that neural networks and k-nearest neighbor achieved the best performance on these datasets, with accuracy rates up to 97.33% for Iris classification. Feature selection techniques like removing zero-weighted attributes were also found to improve some algorithm performance. Predictive analysis experiments found that neural networks and k-nearest neighbor were most accurate at predicting new class labels.
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges involved. It then reviews various existing approaches for clustering uncertain data, including using soft classifiers and probabilistic databases. The document proposes combining k-means clustering with Voronoi diagrams and indexing techniques to improve the performance and efficiency of clustering uncertain datasets. It outlines a plan to integrate k-means with Voronoi diagrams and indexing to reduce execution time and increase clustering performance and results for uncertain data. Finally, it concludes that combining clustering with indexing approaches can better handle uncertain data clustering challenges.
Tracking of Fluorescent Cells Based on the Wavelet Otsu Modelrahulmonikasharma
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With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
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dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A data estimation for failing nodes using fuzzy logic with integrated microco...IJECEIAES
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A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network DatasetsDrjabez
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identifying and removing irrelevant and redundant features. Dataset reduction is important in the case of
machine learning and data mining. The measure of dependence is used to evaluate the relationship
between feature and target concept and or between features for irrelevant and redundant feature removal.
The proposed work initially removes all the irrelevant features and then a minimum spanning tree of
relevant features is constructed using Prim’s algorithm. Splitting the minimum spanning tree based on the
dependency between features leads to the generation of forests. A representative feature from each of the
forests is taken to form the final feature subset
An Analysis of Outlier Detection through clustering methodIJAEMSJORNAL
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SURVEY PAPER ON OUT LIER DETECTION USING FUZZY LOGIC BASED METHOD
1. International Journal on Cybernetics & Informatics (IJCI) Vol. 6, No. 1/2, April 2017
DOI: 10.5121/ijci.2017.6204 29
SURVEY PAPER ON OUT LIER DETECTION USING
FUZZY LOGIC BASED METHOD
Deepa Verma, Rakesh Kumar and Akhilesh Kumar
Department of Information Technology,
Rajkiya Engineering College, Ambedkar Nagar (U.P) – 224 122, India
ABSTRACT
Fuzzy logic can be used to reason like humans and can deal with uncertainty other than randomness.
Outlier detection is a difficult task to be performed, due to uncertainty involved in it. The outlier itself is a
fuzzy concept and difficult to determine in a deterministic way. fuzzy logic system is very promising, since
they exactly tackle the situation associated with outliers. Fuzzy logic that addresses the seemingly
conflicting goals (i) removing noise, (ii) smoothing out outliers and certain other salient feature. This
paper provides a detailed fuzzy logic used for outlier detection by discussing their pros and cons. Thus this
is a very helpful document for naive researchers in this field.
KEYWORDS
Data mining, fuzzy logic, Outlier Detection. Artificial Intelligent Information Systems
1. INTRODUCTION
In a sequence of measurements or generic investigational data the outlier is defined as an
observation that deviates too much from other observations and it is implicit to be generated by a
mechanism different from other observations. The inlier, on the other hand, is defined as an
observation that is explained by underlying probability density function. The exact definition of
an outlier depends on the context [1]. Several definitions have been given and can be roughly
grouped into six categories [2]: I) distribution based, II) depth-based, III) distance-based, IV)
clustering based, V) density-based VI) categorical-based. As a low local density of other data on
the observation is an indication of a possible outlier, all the above listed definitions substantially
come from different strategies for density estimation. The presence of the outliers is not always a
drawback. For instance, in clustering applications, outliers are considered as noise observations
that should be removed in order to perform a more reliable clustering, while in data mining
applications, the detection of anomalous patterns in data is more interesting than the detection of
inliers. In some applications in fault diagnosis, the outliers can be the indicators of a faulty in the
monitored system, thus their presence must be carefully pointed out. When the experimental data
are used to validate a model and/or optimize some of its internal parameters, the presence of
outliers should be avoided and, for this reason, usually a preprocessing of the data is mandatory in
order to filter out such anomalous observation. In the steelmaking field a large amount of data
are collected concerning the chemical composition of a steel cast and its derived products at the
different stages of the manufacture as well as process variables, measurements and analyses
concerning the properties of a each particular manufactured product. While process variables are
normally extracted in an automatic way from the plant information system, some of the results of
the chemical analyses, mechanical tests and inspections are still manually input by the technical
personnel. Errors are thus possible on the values that are recorded by hand, some of which are
quite easily recognized (for instance typographic errors) but most of them are not so easy to point
out. Moreover, anomalous events or faults in the measuring devices as well as in the machinery
2. International Journal on Cybernetics & Informatics (IJCI) Vol. 6, No. 1/2, April 2017
30
itself can results in measurements that are indeed anomalous but do not differ in the order of
magnitude with respect to standard data. Thus their detection cannot be performed via trivial tests
and/or visual inspection, but rather through the implementation of suitable detection strategies,
capable of pointing out anomalous data in an efficient and rapid way. Efficiency and speed are
often conflicting requirements especially when treating big amount of data, as actually in the case
of steelmaking industry us. Fuzzy logic is an approach to computing based on "degrees of truth"
rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is
based. Fuzzy logic is capable of supporting human type of reasoning in natural form to a
considerable extent. It does so by allowing partial membership for data items in fuzzy subsets.
Integration of fuzzy logic with data mining techniques has become one of the key aspects of soft
computing in handling the challenges posed by the massive collection of natural data. The fuzzy
set is different from a crisp set in that it allows the elements to have a degree of membership. The
core of a fuzzy set is its membership function which defines the relationship between a value in
the set’s domain and its degree of membership in the fuzzy set. The relationship is functional
because it returns a single degree of membership for any value in the domain.
2. LITERATURE SURVEY
Crisp set can be used to handle structured data which is generally free from outlier points. But it
is difficult to handle formless natural data which often contain outlier data points. Fuzzy set
(introduced by Zadeh [11]) is different from its crisp counterpart, as it allows the elements to have
a level of membership. Thus, to handle unstructured natural data which is qualitative and
imprecise in nature, many of the data mining techniques are integrated with fuzzy logic [12]. In
traditional clustering approaches each pattern belongs to one and only one cluster [13]. In case of
fuzzy clustering, the membership function associates each pattern with all the clusters. Thus fuzzy
clusters grow in their natural shapes. In this section some fuzzy clustering outlier detection
techniques are analyzed.
Fuzzy C-Means algorithm (FCM) [14]: FCM is one of the well-known fuzzy clustering
algorithms, and used in a wide variety of applications, such as medical imaging, remote sensing,
data mining and pattern recognition [15,16,17,18,19]. Chiu, Yager & Filev have proposed Fuzzy
c-means data clustering algorithm in which each data point belongs to a clustering to a degree
specified by a membership grade [20]. Fuzzy c-means clustering(FCM) has two processes: the
calculation of cluster centers and the assignment of points to these centers using a form of
Euclidian distance. This process is repeated until the cluster centers are stabilized. FCM partitions
a collection of data points into fuzzy groups. Unlike k-means where data point must exclusively
belong to one cluster center, FCM finds a cluster center in each group in order to minimize the
objective function of the dissimilarity measure. FCM employs fuzzy partitioning so that a given
data point can belong to several groups with the degree of belongingness specified by
membership grades between 0 and 1. The membership of each data point corresponding to each
cluster center will be calculated on the basis of distance between the cluster center and the data
point.
Multiple Outlier Detection in Multivariate Data Using Self-Organizing Maps Title [27] Nag
A.K., Mitra A. and Mitra S. proposed an artificial intelligence technique of self-organizing map
(SOM) based non-parametric method for outlier detection. It can be used to detect outliers from
large multidimensional datasets and also provides information about the entire outlier
neighborhood. SOM is a feed forward neural network that uses an unsupervised training
algorithm, and through a process called self-organization, configures the output units into a
topological representation of the original data (Kohonen 1997). SOM produces a topology-
preserving mapping of the multidimensional data cloud onto lower dimensional visualizable plane
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and hence provides an easy way of detection of multidimensional outliers in the data, at
respective levels of influence.
3. DIFFERENT ASPECTS OF AN OUTLIER DETECTION PROBLEM
In this section discusses the different aspects of outlier detection. As mentioned earlier, a specific
formulation of the problem is determined by several different factors such as the input data,
unsupervised data, the availability (or unavailability) of other resources as well as the constraints
and requirements induced by the application domain. This section brings forth the richness in the
problem domain and motivates the need for so many diverse techniques.
(i) input data: a key component of any outlier detection technique is the input data in which it has
to detect the outliers. Input is generally treated as a collection of data objects or data instances
(also referred to as record, point, vector, pattern, event, case, sample, observation, or entity) [tan
et al. 2005a]. Each data instance can be described using a set of attributes (also referred to as
variable, characteristic, feature, field, or dimension). The data instances can be of different types
such as binary, categorical or continuous. Each data instance might consist of only one attribute
(univariate) or multiple attributes (multivariate). In the case of multivariate data instances, all
attributes might be of same type or might be a mixture of different data types.
(ii)Type of Supervision: Besides the input data (or observations), an outlier detection algorithm
might also have some additional information at its disposal. A labeled training data set is one such
information which has been used extensively (primarily by outlier detection techniques based on
concepts from machine learning [Mitchell 1997] and statistical learning theory [Vapnik 1995]). A
training data set is required by techniques which involve building an explicit predictive model.
The labels associated with a data instance denote if that instance is normal or outlier1. Based on
the extent to which these labels are utilized, outlier detection techniques can be divided into three
categories
(ii)a. Supervised outlier detection techniques. First category techniques assume the availability of
a training data set which has labeled instances for normal as well as outlier class. Typical
approach in such case is to build predictive models for both normal and outlier classes. Any
unseen data instance is compared against the two models to determine which class it belongs to.
Supervised outlier detection techniques have an explicit notion of the normal and outlier behavior
and hence accurate models can be built. One drawback here is that accurately labeled training
data might be prohibitively expensive to obtain. Labeling is often done manually by a human
expert and hence requires a lot of effort to obtain the labeled training data set. Certain techniques
inject artificial outliers in a normal data set to obtain a fully labeled training data set and then
apply supervised outlier detection techniques to detect outliers in test data [Abe et al. 2006].
(ii)b. Semi-Supervised outlier detection techniques. Second category of techniques assume the
availability of labeled instances for only one class. it is often difficult to collect labels for other
class. For example, in space craft fault detection, an outlier scenario would signify an accident,
which is not easy to model. The typical approach of such techniques is to model only the
available class and decare any test instance which does not fit this model to belong to the other
class.
(ii)c. Unsupervised outlier detection techniques. The third category of techniques do not make
any assumption about the availability of labeled training data. Thus these techniques are most
widely applicable. The techniques in this category make other assumptions about the data. For
example, parametric statistical techniques, assume a parametric distribution of one or both classes
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of instances. Similarly, several techniques make the basic assumption that normal instances are
far more frequent than outliers. Thus a frequently occurring pattern is typically considered normal
while a rare occurrence is an outlier. The unsupervised techniques typically suffer from higher
false alarm rate, because often times the underlying assumptions do not hold true.
4. OUTLIER DETECTION METHODS IN INDUSTRIAL SENSOR DATA
The occurrence of outliers in industrial data is handle rank deficiency. Comparative studies of
these methods is often the rule rather than the exception. Many standard outlier detection methods
fail to detect outliers in industrial data because of the high data dimensionality. These problems
can he solved by using robust model-based methods that do not require the data to be of full rank.
Jordaan and Smits [5] propose a robust model-based outlier detection approach that exploits the
characteristics of the support vectors extracted by the Support Vector Machine method [6]. The
method makes use of several models of varying complexity to detect outliers based on the
characteristics of the support vectors obtained from SVM-models. This has the advantage that the
decision does not depend on the quality of a single model, which adds to the robustness of the
approach. Furthermore, since it is an iterative approach, the most severe outliers are removed
first. This allows the models in the next iteration to learn from cleaner” data and thus reveal
outliers that were masked in the initial model. The need for several iterations as well as the use of
models, however, makes the on-line application of this method difficult for the not negligible
computational burden. Moreover, if the data to be on-line processes come from dynamic systems,
that tend to change their conditions through time, although not rapidly, the models update is
required and this can furtherly slow the outlier detection. On the other hand, when low
computational burden is required due to strict time constraints, there is a quite wide variety of fast
methods. One of the most commonly used is the Grubbs test [7], a standard and widely applied
statistical procedure, which however, to be appropriately applied, requires that data distribution
can be reasonably approximated by a normal distribution, as it takes into account only the mean
and standard deviation of the data distribution. Also in [8] a fast outlier detection method is
proposed which uses the local correlation integral (LOCI). This method is highly effective for
detecting outliers and groups of outliers, although it takes into consideration only the number of
neighboring points. Neural Networks have been successfully applied for outliers detection, often
coupled with techniques such as Principal Component Analysis (PCA) [9] and Partial Least
Squares (PLS) [10]: for instance in [11] outliers are found by investigating points at the edges of
previously constructed clusters. The neural networks used here have class coded output nodes.
Outliers should not activate any output node. If the maximal output value of the neural network is
less than a rejection threshold, then the input pattern is rejected.
5. APPLICATIONS OF OUTLIER DETECTION
The ability to detect outliers is a highly desirable feature in application domains for a variety of
reasons. In this section we discuss some of the popular applications of outlier detection. For each
application domain we discuss the significance of outliers, the challenges unique to that domain
and the popular approaches for outlier detection that have been adopted in that domain.
(i)Intrusion Detection: Intrusion detection refers to detection of malicious activity (break-ins,
penetrations, and other forms of computer abuse) in a computer related system [Phoha 2002].
These malicious activities or intrusions are very interesting from a computer security perspective.
An intrusion is different from the normal behavior of the system. This property allows a direct
formulation of the problem as an outlier detection problem. Outlier detection techniques have
been widely applied for intrusion detections.
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(ii) Fraud Detection: Fraud detection refers to detection of criminal activities occurring in
commercial organizations such as banks, credit card companies, insurance agencies, cell phone
companies, stock market etc. The malicious users might be the actual customers. of the
organization or might be posing as a customer (also known as identity theft). The fraud occurs
when these users consume the resources provided by the organization in an unauthorized way.
The organizations are interested in immediate detection of such frauds to prevent economic
losses.
6. ADVANTAGES AND DISADVANTAGES OF FUZZY LOGIC BASED METHOD
Advantages:
1. The use of a fuzzy set approach to pattern classification inherently provides degree of
membership information that is extremely useful in higher level decision making.
2. Fuzzy Logic allows you to model in a more intuitive way complex dynamic systems.
3. Fuzzy logic is capable of at the bottom of, human type reasoning in natural form by
allowing partial membership for data items in fuzzy subsets [29].
4. Fuzzy logic can handle uncertainty at various stages.
5. With this approach knowledge can be expressed in terms of If–THEN rules.
Disadvantage:
Disadvantage of fuzzy clustering is that with a growing number of objects the amount of output
of the results becomes huge, so that the information received often cannot be worked.
7. CONCLUSION AND FUTURE SCOPE
In this survey we have discussed different ways in which the problem of outlier detection has
been formulated in literature. outlier detection algorithms are available which are based on fuzzy
logic and have their own advantages and disadvantages. Some researchers have used hybrid
approach, based on fuzzy logic and neural network to combine the advantages and to overcome
the disadvantages of both the techniques for various domain. But very few have used this hybrid
approach for outlier detection. Thus, there is still a wide scope to apply -neural techniques in this
area to improve the quality and required time of outlier detection process. In this paper we
discussed the techniques and methods used by different researchers and basic introduction and
algorithms are discussed in brief. At the end, we conclude that the outlier detection is must to
increase the speed of processing of data and also helpful in reduction of processing cost of data.
We have to compare all the techniques which are effectively used till the time and get the best
one as the result of this comparison and reliable to implement. Try to achieve the objectives
which are defined previously.
REFERENCES
[1] Varun chandola, arindam banerjee and vipin kumar, outlier detection : a survey
[2] Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent and
Fuzzy Systems, 2, 267–278
[3] Hodge, V. and J. Austin, A Survey of Outlier Detection Methodologies, Artificial Intelligence
Review, Vol. 22, 2004, pp. 85–126.
6. International Journal on Cybernetics & Informatics (IJCI) Vol. 6, No. 1/2, April 2017
34
[4] Victoria J. Hodge and Jim Austin, A Survey of Outlier Detection Methodologies, Kluwer Academic
Publishers, 2004.
[5] L.Zadeh, Fuzzy sets, Inform. And control, vol.8, pp. 338-353, ,1965.
[6] Sankar K. Pal, P. Mitra, Data Mining in Soft Computing Framework: A Survey, IEEE transactions on
neural networks, vol. 13, no. 1, January 2002.
[7] E. Cox, Fuzzy Modeling And Genetic Algorithms For Data Mining And Exploration, Elsevier, 2005.
[8] Bezdek, J, L. Hall, and L. Clarke, Review of MR Image Segmentation Techniques Using Pattern
Recognition, Medical Physics, Vol. 20, No. 4, 1993, pp. 1033–1048.
[9] Pham, D, Spatial Models for Fuzzy Clustering, Computer Vision and Image Understanding, Vol. 84,
No. 2, 2001, pp. 285–297.
[10] Rignot, E, R. Chellappa, and P. Dubois, Unsupervised Segmentation of Polarimetric SAR Data Using
the Covariance Matrix, IEEE Trans. Geosci. Remote Sensing, Vol. 30, No. 4, 1992, pp. 697–705.
[11] Al- Zoubi, M. B., A. Hudaib and B Al- Shboul A Proposed Fast Fuzzy C-Means Algorithm, WSEAS
Transactions on Systems, Vol. 6, No. 6, 2007, pp. 1191-1195.
[12] Maragos E. K and D. K. Despotis, The Evaluation of the Efficiency with Data Envelopment Analysis
in Case of Missing Values: A fuzzy approach, WSEAS Transactions on Mathematics, Vol. 3, No. 3,
2004, pp. 656-663.
[13] Binu Thomas and Raju G, A Novel Fuzzy Clustering Method for Outlier Detection in Data Mining,
International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009.
[14] Moh'd Belal Al-Zoubi, Ali Al-Dahoud, Abdelfatah A. Yahya, New Outlier Detection Method Based
on Fuzzy Clustering, WSEAS transactions on information science and applications, Issue 5, Volume
7, May 2010, pp. 681 – 690.
[15] Hawkins, S.; He, X.; Williams, G.J. & Baxter, R.A., Outlier detection using replicator neural
networks. Proceedings of the 5th international conference on Knowledge Discovery and Data
Warehousing, 2002.
[16] R. Hecht-Nielsen. Replicator neural networks for universal optimal source coding. Science, 269
(1860-1863), 1995.
[17] Williams, G.; Baxter, R.; He, H. & Hawkison,S., A comparative study of RNN for outlier detection in
data mining, Proceedings of the IEEE International Conference on Data Mining, pp. 709–712, 9-12
December 2002, Australia.
[18] Nag, A.K.; Mitra, A. & Mitra, S., Multiple outlier Detection in Multivariate Data Using Self-
Organizing Maps Title, Computational Statistical, N.20, 2005, pp.245-264.
[19] Peng Yang, Qingsheng Zhu and Xun Zhong, Subtractive Clustering Based RBF Neural Network
Model for Outlier Detection, Journal Of Computers, Vol. 4, No. 8, August 2009, pp. 755-762.
[20] N. P. Jawarkar, R. S. Holambe and T. K. Basu, Use of fuzzy min-max neural network for speaker
identification, Proc. IEEE Int. Conference on Recent Trends in Information Technology (ICRTIT
2011), MIT, Anna university, Chennai, Jun.-3-5, 2011.
[21] S. S. Panicker, P. S. Dhabe, M. L. Dhore, Fault Diagnosis Using Fuzzy Min-Max Neural Network
Classifier, CiiT International Journal of Artificial Intelligent Systems and Machine Learning, Issue
Jul. 2010. http://www.ciitresearch.org/aimljuly2010.html.
7. International Journal on Cybernetics & Informatics (IJCI) Vol. 6, No. 1/2, April 2017
35
[22] M. Mohammadi, R. V. Pawar, P. S. Dhabe, Heart Diseases Detection Using Fuzzy Hyper Sphere
Neural Network Classifier, CiiT International Journal of Artificial Intelligent Systems and Machine
Learning, Issue July 2010. http://www.ciitresearch.org/aimljuly2010.html.
[23] Wang G., Jinxing Hao, Jian Ma, Lihua Huang, A new approach to intrusion detection using Artificial
Neural Networks and fuzzy clustering. Expert Systems with Applications (2010),
doi:10.1016/j.eswa.2010.02.102
[24] Gath, I and A. Geva, Fuzzy Clustering for the Estimation of the Parameters of the Components of
Mixtures of Normal Distribution, Pattern Recognition Letters, Vol. 9, 1989, pp. 77-86.
[25] Cutsem, B and I. Gath, Detection of Outliers and Robust Estimation using Fuzzy Clustering,
Computational Statistics & Data Analyses, Vol. 15, 1993, pp. 47-61.
AUTHORS
Akhilesh Kumar graduated from Mahatma Ghandhi Mission’s college of Engg. and
technology, Noida, Uttar Pradesh in Computer Science & Engineering in 2010. He has
been M.Tech in the department of Computer Science & Engineering, Kamla Nehru
Institute of Technology, Sultanpur (Uttar Pradesh). SinceAugust2012, he has been with the
Department of Department of Information Technology, Rajkiya EngineeringCollege,
Ambedkar Nagar, as an Assistant Professor.His area of interests includes Computer
Networks and Mobile ad-hoc Nerwork.
Rakesh Kumar was born in Bulandshahr (U.P.), India, in 1984. He received the B.Tech.
degree in Information Technology from Kamla Nehru Institute of Technology, Sultanpur
(U.P.), India, in 2007, and the M.Tech. degrees in ICT Specialization with Software
Engineering from the Gautam Buddha University, Greater Noida, Gautam Budh Nagar,
Uttar Pradesh, India, in 2012.In 2007, he joined the Quantum Technology, New Delhi as a
Software Engineer and Since August 2012, he has been with the Department of
Department of Information Technology, Rajkiya Engineering College, Ambedkar Nagar, as an Assistant
Professor. His current research interests include Computer Network, Multicast Security, Sensor Network
and data mining. He is a Life Member of the Indian Society for Technical Education (ISTE), and he is a
Nominee Member of Computer society of India.