Clustering: A neural network approach

Du, Ke-Lin

doi:10.1016/j.neunet.2009.08.007

Cited by 251 publications

(118 citation statements)

References 176 publications

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“…First, let's note the method [16] for automatic clustering of web documents by relevance to the information needs, using a hybrid neural network. A good survey of the approaches of using neural networks for constructing clusters is available in [17]. So the fine-tuning of the neural network parameters is needed for efficiently clustering for different initial data.…”

Section: Methods Of Researchmentioning

confidence: 99%

Development of prototype for user interface of information system

Dubinsky

2017

TAPR

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Section: Methods Of Researchmentioning

confidence: 99%

Development of prototype for user interface of information system

Dubinsky

2017

TAPR

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“…-fuzzy clustering [2,3]; -partitioning [4]; -artificial neural networks for clustering [5]; -grid-based [6]; -density-based [7]. Density-based clustering methods [7] are divided into two types.…”

Section: Mechanical Engineeringmentioning

confidence: 99%

Grouping Method of Image Fragments of Adjacent Dislocation Etch Pits of the Semiconductor Wafer

Shevchenko

2016

EUREKA: Physics and Engineering

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An increase in production volumes of gallium arsenide semiconductor devices determines the need for better control of dislocations of semiconductor wafer. The grouping method of image fragments of adjacent dislocation etch pits of the semiconductor wafer is proposed in the article. Adjacent fragments will be allocated in the pre-binarized image of wafer surface, which contains adjacent fragments of etch pits of dislocation loops after treatment by the described method. Improved methods for determining the loop line width determines the edge line width of etch pits of suspected dislocations, given the variability of their display in the binarized image. The current loop line width is compared to the reference line width of the dislocation loop. The grouping method of image fragments of adjacent dislocation etch pits of the semiconductor wafer defines recovery of loop lines branching, takes into account various options of line adjacency and determines the direction of further recovery of loop line of dislocation etch pits. A step by step description of the method is given.

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“…Many researchers have worked on Intrusion Detection System to give the preeminent output through their system. It can be more categorized where researchers have mentioned the IDS system with the merging of the artificial neural network and clustering techniques [1,11,12]. They showed the performance with other well-known methods such as decision tree, naïve byes, in terms of detection precision and detection stability.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Network based System for Intrusion Detection using Clustering on Different Feature Selection

Tammi¹,

Nasim²,

Biswas³

et al. 2015

IJCA

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Intrusion Detection System (IDS) is an example of Misuse Detection System that works for detecting malicious attacks. This can be defined as software for security management. Many researchers have proposed the Intrusion Detection System with different techniques to achieve the best accuracy. In this paper it is projected that intrusion detection system with the amalgamation of k-means clustering and artificial neural network to improve the system. To obtain a better result benchmark dataset was split into training and testing part and then cluster the dataset into five different divisions. After getting the cluster data it has been trained by the different Artificial Neural Networks functions as-Feed Forward Neural Network (FFNN), Elman Neural Network (ENN), Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Radial Basis Neural Network (RBNN). After implementing these functions we have proposed a comparative analysis between them and choose the best accuracy rate among them. Here, it has been proved that, using the clustering technique a better accuracy rate can be found that improve the system with the best neural network functions which is the probabilistic neural network. It is also important to select efficient feature sets for better accuracy.

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Clustering: A neural network approach

Cited by 251 publications

References 176 publications

Development of prototype for user interface of information system

Development of prototype for user interface of information system

Grouping Method of Image Fragments of Adjacent Dislocation Etch Pits of the Semiconductor Wafer

Artificial Neural Network based System for Intrusion Detection using Clustering on Different Feature Selection

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