Kernel Fuzzy Clustering Algorithm Research Articles

Research on Audit Opinion Prediction of Listed Companies Based on Sparse Principal Component Analysis and Kernel Fuzzy Clustering Algorithm

The prediction of audit opinions of listed companies plays a significant role in the security market risk prevention. By introducing machine learning methods, many innovations can be implemented to improve audit quality, lift audit efficiency, and cultivate the keen insight of auditors. However, in a realistic environment, category imbalance and critical feature selection exist in the prediction model of company audit opinions. This paper firstly combines batched sparse principal component analysis (BSPCA) with kernel fuzzy clustering algorithm (KFCM) and proposes a sparse-kernel fuzzy clustering undersampling method (S-KFCM) to deal with the imbalance of sample categories. This method adopts the kernel fuzzy clustering algorithm to down-sample the normal samples, and their features are extracted from abnormal sample sets based on the group sparse component method. The sparse normal sample set can maintain the original distribution space structure and highlight the classification boundary samples. Secondly, considering the company’s characteristic attributes and data sources, 448 original variables are grouped, and then BSPCA is used for feature screening. Finally, the support vector machine (SVM) is adopted to complete the classification prediction. According to the empirical results, the SKFCM-SVM model has the highest prediction accuracy.

Internet of things network intrusion detection model based on quantum artificial fish group and fuzzy kernel clustering algorithm

AbstractThe application of internet of things (IoT) devices in 5G/B5G has benefited many industries, such as automated production, smart agriculture and autopilot, and has provided access to billions of smart devices. However, the rapid growth of IoT devices has also brought network security issues. The purpose of this paper is to study the network intrusion detection model based on quantum artificial fish population and fuzzy kernel clustering algorithm. In this paper, the network intrusion detection model based on traditional fuzzy C‐means (FCM) clustering has poor classification effect and is prone to local extremum. A semi‐supervised fuzzy kernel–clustering algorithm based on quantum artificial fish group is proposed. The algorithm adopts a small amount of tag data and many unknown tag data to generate the classification of network intrusion detection and constructs a new objective function of FCM algorithm by means of kernel distance. In the simulation experiment of KDD Cup 99 network intrusion detection data, compared with the intrusion detection model based on FCM, particle swarm optimization‐FCM, the detection rate of the proposed algorithm in the known attack and unknown attack is 92.5% and 84.9%, respectively. The results show that the proposed intrusion detection algorithm has better detection rate.

The intrusion data mining method for distributed network based on fuzzy kernel clustering algorithm

In order to overcome the shortcomings of intrusion data mining method, which is easy to fall into local extremum and leads to poor mining effect, a distributed network intrusion data mining method based on fuzzy kernel clustering algorithm is studied. In this method, a new feature vector is established by using the Gauss kernel function which accords with Mercer condition through the fuzzy kernel clustering algorithm, so that the data pattern space can be effectively mapped to the high-dimensional feature space. When the new data does not meet the clustering condition, a new cluster set and sub box are established, until the end of all data clustering, the distributed network intrusion data mining is realised. The experimental results show that the accuracy of mining is higher than 95%, the false alarm rate and the false alarm rate are lower than 2%, and the total mining time is only 446 ms.

Spatial information-based intuitionistic fuzzy kernel clustering algorithm for cerebral hemorrhage image segmentation

Spatial information-based intuitionistic fuzzy kernel clustering algorithm for cerebral hemorrhage image segmentation

WITHDRAWN: A new proposal for the segmentation of breast lesion in mammogram images using optimized kernel fuzzy clustering algorithm

Abstract Breast Cancer is one of the fatal diseases which is caused due to the un natural development of the tissues in the breasts which are abnormal. The level of sarcoma or the stage of the cancer is mostly relied upon the doctor’s analysis. In order to provide a technical contribution which supports the doctor to take decision, this paper is intended to develop a framework which can help in determining the stage in which the cancer is at present. The major issues in the prediction of breast cancer through mammograms are the diverged artifacts, similar breast tissues and lower contrast on the boundary between skin and air. To overcome these issues, Optimized Kernel Fuzzy Clustering Algorithm is developed (OKFCA) and to determine the cancer portions in mammogram images. The OKFCA algorithm has described to identify the segmented regions in Mammogram Image Analysis Society (MIAS) database. The proposed segmentation algorithm is carried out with pre-processed mammogram images, noise free image that was obtained by using Hybrid Denoising Filter (HDF) algorithm and the proposed OKFCA is a significant approach to find out the cancer segment of mammogram image. Data clustering facilitates to place data of similar types in one group and of dissimilar types in different group. The results from the experiments which were carried on the MIAS data confirms the efficiency of the proposed system in terms of accuracy when compared to that of the famous K-Means, OKFCA and Otsu methods.

Empirical Analysis of Stock Investment Based on β-KFCM

Cluster analysis is an analysis method that classifies data sets. It measure the similarity between different data and classifies them on the basis of similarity, but the classification is unknown. In recent years, with the popularization and improvement of information technology, a large number of diversified data have been produced in various industries. In order to extract data features and classify them, this paper, based on the study of fuzzy C-means clustering algorithm, take the systematic risk coefficient as the weight, expand the model to get the weighted fuzzy kernel clustering algorithm: -KFCM fuzzy clustering algorithm and solve the algorithm. Based on the empirical research, we choose six Accounting Indicators to study 50 stocks in A-share market, including current ratio, cash ratio, year-on-year growth rate of operating revenue, return rate of total assets, net sales interest rate and asset liability ratio. According to the empirical research, we can get the results that the investment grade is divided into three categories, among them, Category I is the recommended high-quality investment variety, category II and category III are next to it.

Application of artificial fish swarm optimization semi-supervised kernel fuzzy clustering algorithm in network intrusion

For the unsupervised learning based clustering algorithm, the intrusion detection rate is low, and the training sample based on supervised learning clustering algorithm is insufficient. A semi-supervised kernel fuzzy C-means clustering algorithm based on artificial fish swarm optimization (AFSA-KFCM) is proposed. Firstly, the kernel function is used to change the distance function in the traditional semi-supervised fuzzy C-means clustering algorithm to define a new objective function, thus improving the probabilistic constraints of the fuzzy C-means algorithm. Then, the artificial fish swarm algorithm with strong global optimization ability is used to improve the KFCM sensitivity to the initial cluster center and easy to fall into the local extremum, thus improving the convergence speed and improving the classification effect. The test results in the Wine and IRIS public datasets show that the AFSA-KFCM clustering algorithm is superior to the traditional algorithm in clustering accuracy and time efficiency. At the same time, the experimental results in KDDCUP99 experimental data show that the algorithm can obtain the ideal detection rate and false detection rate in intrusion detection.

Detection of Breast Lesion Using Improved GLCM Feature Based Extraction in Mammogram Images

A classic mammogram image processing method commonly illustrates of mammogram image pre-processing, segmentation, feature extraction and classification techniques. Feature extraction is a significant measure in Computer Assisted Diagnosis (CAD) of Breast cancer abnormalities. Feature Extraction is the method of shrinking the dimension of image data by finding an essential detail as of the segmented image. The visual substance of a segmented image portions can be confined using this procedure. Feature extraction is one of the Image retrieval methods for achieving higher accuracy. In this paper an Improved Gray Level Co-occurrence Matrix (GLCM) Feature based Extraction with shape and Tamura features are discussed. Tamura features are extracted from Optimized Kernel Fuzzy Clustering Algorithm (OKFCA) which is one of the segmentation methods. This paper proposed Improved GLCM Feature Based Extraction algorithm to obtain the trained features which will be used for further processing in future. Experimental results of 322 images were evaluated from Mammographic Image Analysis Society (MIAS) database and used to evaluate the feature extraction process.

Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on KDFcmPUM.

This paper proposes a partition of unity method (PUM) based on KDFCM (KDFcmPUM) that can be implemented to solve the dense matrix problem that occurs when the radial basis function (RBF) interpolation method deals with a large amount of scattered data. This method introduces a kernel fuzzy clustering algorithm to improve clustering accuracy and achieve the partition of unity. The local compact support RBF is used to construct the weight function, and local expression is obtained from the interpolation of the global RBF. Finally, the global expression is constructed by the weight function and local expression. In this paper, the method is applied to the orthogonally splitting imaging pose sensor to establish the mathematical model and the calibration and test experiments are carried out. The calibration and test accuracy both reached ±0.1 mm, and the number of operations was reduced by 4% at least. The experimental results show that KDFcmPUM is effective.

An expert weighting method based on affinity propagation clustering algorithm

Since the expert weighting method based on fuzzy kernel clustering algorithm is sensitive to initializing the clustering center, it is difficult to converge the iterative results to the global optimum. In this paper, an expert weighting method based on affinity propagation (AP) clustering algorithm is proposed to solve the problem of unreasonable clustering. This method uses AP clustering algorithm to cluster experts, since the number of clusters does not need to be set in advance, the clustering result is prevented from being limited by the selection of the initial cluster center, and improves the rationality of clustering. At the same time, the deviation entropy model is constructed to obtain the weights of experts, and effectively avoids the problem of the weighting method based on consistency ratio and information entropy that the consistency ratio and information entropy are equal but the expert opinions are different, and makes the expert weighting more scientific. The example indicates that the proposed method is feasible and effective.

Kernel Fuzzy Clustering with Output Layer Self-Connection Recurrent Neural Networks for Software Cost Estimation

In the current world, the software cost estimation problem has been resolved using various newly developed methods. Significantly, the software cost estimation problems can be dealt with effectively with the recently grown recurrent neural network (RNN) than the other newly developed methods. In this paper, an improved approach is proposed to software cost estimation using Output layer self-connection recurrent neural networks (OLSRNN) with kernel fuzzy c-means clustering (KFCM). The proposed OLSRNN method follows the basics of traditional RNN models for integrating self-connections to the output layer; thereby, the output temporal dependencies are better captured. Also, the performance of neural networks is improved using the kernel fuzzy clustering algorithm to enhance software estimation results. Ultimately, five publicly available software cost estimation datasets are adapted to verify the efficacy of the proposed KFCM-OLSRNN method using the validation metrics such as MdMRE, PRED (0.25) and MMRE. The experimental results proved the efficiency of the proposed method for solving the software cost estimation problem.

An intelligence evaluation method of the environmental impact for the cutting process

Environmental impact evaluations in manufacturing fields is a complicated decision-making problem, which involves wastes emission, resource consumption and energy utility, etc. This paper presents an intelligence method for environmental impact evaluation using kernel fuzzy clustering and a back-propagation neural network. The objective of this article is to apply artificial intelligence technology to evaluate environmental influence of cutting process and develop a decision support tool for selecting the optimal environmental solution from various alternative schemes. There are three stages to accomplish this assessment procedure. First, the evaluation index system of the cutting process was analyzed and established. Next, a training sample set which was used for learning the ‘knowledge and experience’ by neural networks, was acquired by kernel fuzzy clustering algorithm. Finally, an evaluation model based on a back-propagation neural network was constructed, and connection weights of the model were determined after training. A case research on the cutting processes of the automobile workpiece was conducted. Results showed that the proposed method is more concise and practicable than existing evaluation methods and provide a feasible and effective decision-making tool for selecting an optimal cutting process while manufacturing.

Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network

This study introduces a novel self-organizing recurrent interval type-2 fuzzy neural network (SRIT2FNN) for the construction of a soft sensor model for a complex chemical process. The proposed SRIT2FNN combines interval type-2 fuzzy logic systems (IT2FLSs) and recurrent neural networks (RNNs) to improve the modeling precision. The Gaussian interval type-2 membership function is used to describe the antecedent part of the SRIT2FNN fuzzy rule, and the consequent part is of the Mamdani type with an interval random number. An adaptive optimal clustering number of fuzzy kernel clustering algorithm based on a Gaussian kernel validity index (GKVI-AOCN-FKCM) is developed to determine the structure of the SRIT2FNN and fuzzy rule antecedent parameters, and the parameter learning of SRIT2FNN used the gradient descent method. Finally, the proposed SRIT2FNN is applied to the soft sensor modeling of ethylene cracking furnace yield in a typical chemical process. Comparisons between the SRIT2FNN and conventional fuzzy neural network (FNN) and interval type-2 fuzzy neural network (IT2FNN) are made via simulation experiments. The results show that the proposed SRIT2FNN performs better than the conventional FNN and IT2FNN.

Reformulated Query based Document Retrieval using optimized Kernel Fuzzy Clustering algorithm

Clustering-based document retrieval system offers to find similar documents for a given user's query. This study explores the scope of kernel fuzzy c-means (KFCM) with the genetic algorithm on document retrieval issue. Initially, genetic algorithm-based kernel fuzzy c-means algorithm (GKFCM) is proposed to make the clustering of documents in the library. For each cluster, an index is created, which contains a common significant keywords of the documents for that cluster. Once the user enters the keyword as the input to the system, it will process the keywords with the WORDNET ontology to achieve the neighbourhood keywords and related synset keywords. Lastly, the documents inside the cluster are released at first as the resultant-related documents for the query keyword, which clusters have the maximum matching score values. Experiments results prove that GKFCM-based proposed system outperforms better performance than existing methods.

A novel kernel fuzzy clustering algorithm for Geo-Demographic Analysis

Geo-Demographic Analysis (GDA) is a major concentration of various interdisciplinary researches and has been used in many decision-making processes regarding the provision and distribution of products and services in society. Machine learning methods namely Principal Component Analysis, Self-Organizing Map, K-Means, fuzzy clustering and fuzzy geographically weighted clustering were proposed to enhance the quality of GDA. Among them, the state-of-the-art method – Modified Intuitionistic Possibilistic Fuzzy Geographically Weighted Clustering (MIPFGWC) has some drawbacks such as: (i) using the Euclidean similarity measure often results in high error rate and sensitivity to noises and outliers; (ii) updating the membership matrix by the Spatial Interaction – Modification Model (SIM2) model leads to new centers not being “geographically aware”. In this paper, we present a novel fuzzy clustering algorithm named as Kernel Fuzzy Geographically Clustering (KFGC) that utilizes both the kernel similarity function and the new update mechanism of the SIM2 model to remedy the disadvantages of MIPFGWC. Some supported properties and theorems of KFGC are also examined in the paper. Specifically, the differences between solutions of KFGC and those of MIPFGWC and of some variants of KFGC are theoretically validated. Lastly, experimental analysis is performed to compare the performance of KFGC with those of the relevant algorithms in terms of clustering quality.

Fire Seat Intelligent Identification System

Current fire alarm system can only send fire alarms but failed to report accurately ignition point. So a fire seat intelligent identification system was designed based on wireless sensor networks. The system collects data from wireless sensors using ZigBee wireless communication technology, and carries on cluster analysis on the data set using the improved fuzzy kernel clustering algorithm, and gets accurate clustering results. Finally the ignition source location information is reported to the fire alarm control center. The experimental results show that, compared with other methods, this system realizes real-time monitoring and automatic identification of fire seats, which has virtues of high sensitivity and accuracy and high-speed data transfer.

Spatial Information Based on the Multi-Objective Programming of Fuzzy Kernel Clustering Image Segmentation Algorithm

Fuzzy kernel clustering algorithm is a combination of unsupervised clustering and fuzzy set of the concept of image segmentation techniques, But the algorithm is sensitive to initial value, to a large extent dependent on the initial clustering center of choice, and easy to converge to local minimum values, when used in image segmentation, membership of the calculation only consider the current pixel values in the image, and did not consider the relationship between neighborhood pixels, and so on segmentation contains noise image is not ideal. This paper puts forward an improved fuzzy kernel clustering image segmentation algorithm, the multi-objective problem, change the single objective problem to increase the secondary goals concerning membership functions, Then add the constraint information space; Finally, using spatial neighborhood pixels corrected membership degree of the current pixel. The experimental results show that the algorithm effectively avoids the algorithm converges to local extremism and the stagnation of the iterative process will appear problem, significantly lower iterative times, and has good robustness and adaptability.

A Fuzzy Kernel Clustering Algorithm based on Improved Rough Set Attribute Reduction

A Fuzzy Kernel Clustering Algorithm based on Improved Rough Set Attribute Reduction

Improved kernel-based fuzzy clustering algorithm

Since the traditional kernel fuzzy clustering algorithm does not take account of the level of contribution the every feature makes toward clustering,and it also has a shortcoming of easy falling into a situation of a local optimum,an improved kernel fuzzy clustering algorithm was put forward.It combined the advantage of the global optimum to construct a simple and effective fitness function that can avoid plunging local optimum.This improved algorithm gave every feature a weighted coefficient,in which the ReliefF algorithm was used to assign the weights for every feature.Compared with the traditional algorithm,this one has made some significant progress,and the experimental result has proved its effectiveness.