clustering is an important technique in data mining. The innovative algorithm proposed in this paper obtains clusters by first identifying boundary points as opposed to existing methods that calculate core cluster poi...
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clustering is an important technique in data mining. The innovative algorithm proposed in this paper obtains clusters by first identifying boundary points as opposed to existing methods that calculate core cluster points before expanding to the boundary points. To achieve this, an affine space-based boundary detection algorithm was employed to divide data points into cluster boundary and internal points. A connection matrix was then formed by establishing neighbor relationships between internal and boundary points to perform clustering. Our clustering algorithm with an affine space-based boundary detection algorithm accurately detected clusters in datasets with different densities, shapes, and sizes. The algorithm excelled at dealing with high-dimensional datasets.
Cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzy c-m...
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Cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzy c-means algorithm was adapted for directional data. In the literature, several methods have been used for the clustering of directional data. Due to the use of trigonometric functions in these methods, clustering is performed by approximate distances. As opposed to other methods, the FCM4DD uses angular difference as the similarity measure. Therefore, the proposed algorithm is a more consistent clustering algorithm than others. The main benefit of FCM4DD is that the proposed method is effectively a distribution-free approach to clustering for directional data. It can be used for N-dimensional data as well as circular data. In addition to this, the importance of the proposed method is that it would be applicable for decision making process, rule-based expert systems and prediction problems. In this study, some existing clustering algorithms and the FCM4DD algorithm were applied to various artificial and real data, and their results were compared. As a result, these comparisons show the superiority of the FCM4DD algorithm in terms of consistency, accuracy and computational time. Fuzzy clustering algorithms for directional data (FCM4DD and FCD) were compared according to membership values and the FCM4DD algorithm obtained more acceptable results than the FCD algorithm. (C) 2016 Elsevier Ltd. All rights reserved.
Underwater sensor networks (UWSN) often suffers from the irreplaceable batteries and high delay of long-distance communications, thus one of the most important issues on UWSN is how to extend the lifespan of the netwo...
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Underwater sensor networks (UWSN) often suffers from the irreplaceable batteries and high delay of long-distance communications, thus one of the most important issues on UWSN is how to extend the lifespan of the network and balance the energy consumption of each node by reducing the transmission distances. Actually, clustering method is one of the main methods to resolve the problem. In the clustered UWSN, the major concerns are obtaining appropriate number of clusters, forming the clusters and selecting an optimal cluster head(CH) with each cluster. This paper proposes a novel hybrid clustering method based on fuzzy c means (FCM) and moth-flame optimization method (MFO) to improve the performance of the network(FCMMFO). The idea is to form energy-efficient clusters by using FCM and then use an optimization algorithm MFO to select the optimal CH within each cluster. The simulation results validate the energy-efficient performance of FCMMFO in comparison with the other existing algorithms. The results clearly show the significant impact of FCMMFO on energy-efficiency in UWSN.
In this paper we propose a novel fuzzy hybrid quantum artificial immune clustering algorithm based on cloud model (C-FHQAI) to solve the stochastic problem. Fuzzy hybrid quantum artificial immune algorithm can be deve...
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In this paper we propose a novel fuzzy hybrid quantum artificial immune clustering algorithm based on cloud model (C-FHQAI) to solve the stochastic problem. Fuzzy hybrid quantum artificial immune algorithm can be developed with some of the advantages of information processing where there is a certain amount of indeterminism with qubits, i.e. quantum bits, replacing classical neurons having deterministic states and also in place of the classical artificial immune algorithm with quantum operators. The fuzzy combinatorial fuzzy hybrid quantum artificial immune clustering algorithm (C-FHQAI) is more expressive than the other fuzzy theories and methods. Finally, numerical examples show that the clustering effectiveness of the C-FHQAI algorithm is fast convergence and improves the accuracy of the fuzzy calculation. We find that the C-FHQAI clustering algorithm has the perspective of widespread application. (C) 2014 Elsevier Ltd. All rights reserved.
At present, the dairy brand loyalty evaluation model is not perfect, and the dairy brand loyalty measurement model for the consumer-oriented industry needs to be further studied. Through machine learning methods, onli...
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At present, the dairy brand loyalty evaluation model is not perfect, and the dairy brand loyalty measurement model for the consumer-oriented industry needs to be further studied. Through machine learning methods, online consumer brand product purchase behaviors are clustered to achieve clustering of users with similar loyalty and to measure online dairy brand loyalty. This study has the advantages of applying machine learning to processing online consumer big data, that is, it has advantages when processing high-dimensional data, when processing data in multiple ways, and when analyzing data with high complexity algorithms. The independent variables, dependent variables, and adjusted variables in the model are measured in the form of a Likert five-level scale. Moreover, this study combines with actual cases to make adjustments to the measurement of dairy brand loyalty and verifies the model performance through simulation experiments. The research results show that the validity of the scale structure is good, and the research model has certain practical effects.
Techniques for analyzing genome sequences in high performance environments to predict the function and structure of a protein have been developing. The function of a protein is determined by its characteristics and th...
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Techniques for analyzing genome sequences in high performance environments to predict the function and structure of a protein have been developing. The function of a protein is determined by its characteristics and the sequence pattern, and a protein is classified as belonging to a family according to its genealogy and structure. This study determines the protein family of unknown proteins by analyzing the sequence database of the proteins, which is classified using a clustering algorithm. The analysis of the experimental clustering results verified that, by applying the proposed pf_cluster algorithm, the protein family of new proteins can be found using their sequence information.
This paper presents a variable-categorized clustering algorithm (VCCA) using fuzzy logic for Internet of Things (IoT) local networks. The VCCA selects the cluster head (CH) that has the highest network capacity throug...
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This paper presents a variable-categorized clustering algorithm (VCCA) using fuzzy logic for Internet of Things (IoT) local networks. The VCCA selects the cluster head (CH) that has the highest network capacity through a classification process of cluster variables in accordance with the characteristics in order to configure a clustered network, which differs for different IoT applications. To achieve this, the VCCA employs a fuzzy inference system (FIS) that calculates an outcome through rule-based variable mapping for low complexity in the CH election and high scalability of cluster variables. In addition, experimental simulations using MATLAB are conducted to evaluate the performance of the VCCA. The simulation results show that the VCCA exhibits better network performance compared to the existing algorithms in terms of throughput, end-to-end latency, network lifetime, and energy consumption.
The concern of this work is global optimization using genetic algorithms (GAs). In this work we propose a synergy between the cluster analysis technique, popular in classical stochastic global optimization, and the GA...
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The concern of this work is global optimization using genetic algorithms (GAs). In this work we propose a synergy between the cluster analysis technique, popular in classical stochastic global optimization, and the GA to accomplish global optimization. This synergy minimizes redundant searches around local optima and enhances the capability of the GA to explore new areas in the search space. The proposed methodology demonstrates superior performance when compared with the simple GA on benchmark cases. We also report our solution of the optimal pumps configuration synthesis problem. (C) 1998 Elsevier Science Ltd. All rights reserved.
The advancement of data mining technology presents a way to examine and analyse the medical databases. Microarray data help in analysing the gene expressions, and the process of clustering helps in categorizing the da...
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The advancement of data mining technology presents a way to examine and analyse the medical databases. Microarray data help in analysing the gene expressions, and the process of clustering helps in categorizing the data into organized groups. Grouping similar gene expressions paves the way for effective analysis, and the relationship between the expressions can be figured out. Recognizing the benefits of clustering, this work intends to present a clustering algorithm by combining generalized hierarchical fuzzy C means (GHFCM) and grey wolf optimization (GWO) algorithms. The GWO algorithm is utilized for selecting the initial clustering point, and the GHFCM algorithm is employed for clustering the microarray gene data. The performance of the proposed clustering algorithm is tested with respect to precision, recall,F-measure and time consumption, and the results are compared with the existing approaches. The performance of the proposed work is satisfactory with betterF-measure rates and minimal time consumption.
Environmental pollution has become an issue of serious international concern in recent years. Among the receptor-oriented pollution models, CMB, PMF, UNMIX, and PCA are widely used as source apportionment models. To i...
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Environmental pollution has become an issue of serious international concern in recent years. Among the receptor-oriented pollution models, CMB, PMF, UNMIX, and PCA are widely used as source apportionment models. To improve the accuracy of source apportionment and classify the sample data for these models, this study proposes an easy-to-use, high-dimensional EPC algorithm that not only organizes all of the sample data into different groups according to the similarities in pollution characteristics such as pollution sources and concentrations but also simultaneously detects outliers. The main clustering process consists of selecting the first unlabelled point as the cluster centre, then assigning each data point in the sample dataset to its most similar cluster centre according to both the user-defined threshold and the value of similarity function in each iteration, and finally modifying the clusters using a method similar to k-Means. The validity and accuracy of the algorithm are tested using both real and synthetic datasets, which makes the EPC algorithm practical and effective for appropriately classifying sample data for source apportionment models and helpful for better understanding and interpreting the sources of pollution. (C) 2015 Elsevier Ltd. All rights reserved.
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