Outlier Detection using Improved Genetic K-means

Marghny, M. H.; Taloba, Ahmed I.

doi:10.5120/3458-4723

Cited by 16 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tam tersi durumda, yani en aykırı verinin diğer tüm verilere yakın olduğu durumlarda, istenenden çok daha fazla verinin aykırı veri olarak tespit edilmesi durumu ortaya çıkabilmektedir. Aynı metot 2015 yılında Marghny ve Taloba tarafından bu kez genetik k-ortalama algoritması üzerinde kullanılmıştır [19].…”

Section: öNceki çAlışmalarunclassified

A cluster based approach to reduce pattern layer size for generalized regression neural network

Oral¹,

Kartal²,

Özyildirim³

2018

Pamukkale J Eng Sci

View full text Add to dashboard Cite

Genelleştirilmiş Regresyon Yapay Sinir Ağı (GRYSA) radyal tabanlı çalışan ve genellikle tahminleyici olarak kullanılan denetimliöğrenimli bir yapay sinir ağı (YSA) modelidir. Kolay modellenebilmesinin yanında hızlı ve tutarlı sonuçlar üretmesi bu algoritmanın güçlü yanlarını oluşturmaktadır. Ancak GRYSA tahmin mekanizmasında, eğitim veri setindeki her örnek veri için örüntü katmanında bir adet nöron tutulmaktadır. Bu nedenle, eğitim veri setinin çok büyük olduğu çalışmalarda örüntü katman yapısı örnek verilerinin sayısıyla aynı oranda büyümekte, yapılan işlem sayısı ve bellek gereksinimi artmaktadır. Bu çalışmada, GRYSA algoritmasının işlem sayısını azaltmaya yönelik olarak literatürde daha önce de denenmiş olan k-ortalama kümeleme algoritması ön-işlemci olarak kullanılmış, literatürdeki çalışmalardan farklı olarak, bu çalışmaların performansını negatif anlamda etkileyen kümeler arasına düşen test verileri bulunarak aykırı veri oluşmasının önüne geçilmiştir. Böylece, örüntü katmanındaki bellek ihtiyacı ve işlem sayısı azaltılırken, kümeleme algoritmasının eklenmesi ile performansta ortaya çıkan negatif etki büyük oranda giderilmiş ve yaklaşık %90 daha az eğitim verisi ile neredeyse aynı tahmin sonuçları elde edilmiştir. Generalized Regression Neural Network (GRNN), is a radial basis function based supervised learning type Artificial Neural Network (ANN) which is commonly used for data predictions. In addition to its easy modelling structure, being fast and producing accurate results are the other strong features of it. On the other hand, GRNN employs a neuron in pattern layer for each data sample in training data set. Therefore, for huge data sets pattern layer size increases proportional to the number of samples in training data set, memory requirement and computational time also increase excessively. In this study, in order to reduce space and time complexity of GRNN, k-means clustering algorithm which had been used as pre-processor in the literature is utilized and outlier data emergence which affects the performances of previous studies negatively, is prevented by identifying test data located between clusters. Hence, while memory requirement in pattern layer and number of calculations are reduced, negative effect on the performance emerged by the use of clustering algorithm is significantly removed and almost the same prediction performances to that of standard GRNN are achieved by using 90% less training samples.

show abstract

Section: öNceki çAlışmalarunclassified

A cluster based approach to reduce pattern layer size for generalized regression neural network

Oral¹,

Kartal²,

Özyildirim³

2018

Pamukkale J Eng Sci

View full text Add to dashboard Cite

show abstract

“…outlier removal clustering. But this can work for large scale data of same type not for mixed type [19].…”

Section: Related Workmentioning

confidence: 99%

Outlier Reduction using Hybrid Approach in Data Mining

Lekhi¹,

Mahajan²

2015

IJMECS

View full text Add to dashboard Cite

The Outlier detection is very active area of research in data mining where outlier is a mismatched data in dataset with respect to the other available data. In existing approaches the outlier detection done only on numeric dataset. For outlier detection if we use clustering method , then they mainly focus on those elements as outliers which are lying outside the clusters but it may possible that some of the unknown elements with any possible reasons became the part of the cluster so we have to concentrate on that also. The Proposed method uses hybrid approach to reduce the number of outliers. The number of outlier can only reduce by improving the cluster formulation method. The proposed method uses two data mining techniques for cluster formulation i.e. weighted k-means and neural network where weighted kmeans is the clustering technique that can apply on text and date data set as well as numeric data set. Weighted kmeans assign the weights to each element in dataset. The output of weighted k-means becomes the input for neural network where the neural network is the classification and clustering technique of data mining. Training is provided to the neural network and according to that neurons performed the testing. The neural network test the cluster formulated by weighted k-means to ensure that the clusters formulated by weighted k-means are group accordingly. There is lots of outlier detection methods present in data mining. The proposed method use Integrating Semantic Knowledge (SOF) for outlier detection. This method detects the semantic outlier where the semantic outlier is a data point that behaves differently with other data points in the same class or cluster. The main motive of this research work is to reduce the number of outliers by improving the cluster formulation methods so that outlier rate reduces and also to decrease the mean square error and improve the accuracy. The simulation result clearly shows that proposed method works pretty well as it significantly reduces the outlier.

show abstract

“…In order to find hidden patterns of data and convert them into useful knowledge, this is known Data Mining. This direction includes methods other than classical analysis, based on clustering analysis [1][2][3][4], classification analysis [5,6], and solving problems of generalization, association and finding patterns [7][8][9]. This area of research has recently become more and more important.…”

Section: Introductionmentioning

confidence: 99%

Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine

Marghny¹,

El-Aziz²,

Taloba³

2014

IJCA

View full text Add to dashboard Cite

Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM needs the solution of a quadratic program which require specialized code. In addition, SVM has many parameters, which affects the performance of SVM classifier. Recently, the Generalized Eigenvalue Proximal SVM (GEPSVM) has been presented to solve the SVM complexity. In real world applications data may affected by error or noise, working with this data is a challenging problem. In this paper, an approach has been proposed to overcome this problem. This method is called DSA-GEPSVM. The main improvements are carried out based on the following: 1) a novel fuzzy values in the linear case. 2) A new Kernel function in the nonlinear case. 3) Differential Search Algorithm (DSA) is reformulated to find near optimal values of the GEPSVM parameters and its kernel parameters. The experimental results show that the proposed approach is able to find the suitable parameter values, and has higher classification accuracy compared with some other algorithms.

show abstract

Outlier Detection using Improved Genetic K-means

Cited by 16 publications

References 14 publications

A cluster based approach to reduce pattern layer size for generalized regression neural network

A cluster based approach to reduce pattern layer size for generalized regression neural network

Outlier Reduction using Hybrid Approach in Data Mining

Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine

Contact Info

Product

Resources

About