Narrow Results

Traditional innovation and entrepreneurship education quality evaluation methods often use simple index weighting or cluster analysis, ignoring the correlation and fuzziness between the indicators, resulting in lower credibility of education quality evaluation results. Therefore, an innovation and entrepreneurship education quality evaluation model based on fuzzy clustering is proposed. After analyzing the basic characteristics and influencing factors of innovation and entrepreneurship education, the evaluation index system of innovation and entrepreneurship education is constructed from multiple perspectives according to the principles of scientificity, comprehensiveness, accuracy and operability. In order to improve the consistency of quality data of innovation and entrepreneurship education, it is normalized. The projection pursuit technique is used to reduce dimension and fuzzy classification of multi-dimensional time series education data set, and fuzzy rules are extracted according to classification results and optimal projection values. Three fuzzy membership functions are generated by the trapezoidal distribution method. Finally, the evaluation level of education quality sample data is determined according to the fuzzy approximation degree. The test results show that the construction time of the evaluation model is short, and the reliability of the quality evaluation of innovation and entrepreneurship education is improved.

Addressing the challenges of inadequate generalization ability and inability to effectively manage fuzzy data is indispensable to improve the precision of journey route recommendations within the field of travel advice structures. To address these problems, this paper introduces a revolutionary algorithm that amalgamates fuzzy clustering with attribute-based characteristic integration for the recommendation of tour routes. Leveraging the adeptness of the bushy C-capability clustering set of rules in managing vague and ambiguous information, this approach is meticulously mixed with function fusion strategies to significantly augment the personalization and accuracy of the recommendation gadget. The proposed methodology encompasses a systematic workflow beginning with facts preprocessing, followed by way of an evaluation of consumer choices through fuzzy clustering to perceive wonderful consumer businesses. Eventually, travel destinations are also clustered based on attributes such as geographical region and cultural characteristics. Necessary characteristic functions are extracted from each person behavior and travel vacation spot data, forming the idea for a fusion version that optimizes the advice manner. Implementing this set of rules results in a polished advice machine that no longer only addresses the restrictions of present fashions but also demonstrates a marked improvement in recommending travel routes that align carefully with personal choices. This advancement has great implications for boosting personal satisfaction and engagement in the context of travel planning.

An essential step in the agricultural monitoring system is identifying the crop type. However, the limiting physical features of images, as well as the lack of sufficient detail in a single-temporal image, limit their potential for crop mapping. Furthermore, time-series Synthetic Aperture Radar (SAR) data may not be compatible with the existing approaches. Therefore, a novel approach to crop-type identification needs to be put forth in order to solve the aforementioned problems. To this extent, the presented proposed framework introduces a deep learning (DL) technique using the advanced framework for accurate crop mapping. At first, the input dataset is pre-processed based on data mining techniques like normalization and cleaning. Afterward, Bidirectional Gated Auto encoders (BiGAE) features are extracted from the pre-processed data. Consequently, feature selection uses an opposition learning-based mud ring algorithm (Opp-MR) to reduce the redundant data. Then, the selected data are clustered based on relevance using an adaptive Kernel fuzzy clustering (AkFC) technique. Finally, the goshawk integrated convolutional attention efficient net (GICANet) is performed using an advanced DL framework to map crops accurately. The performances are evaluated using the Python simulation platform. The proposed method improves the overall accuracy by 97.74%, whereas the existing models like ResNet, DenseNet, EfficientNetB0, EfficientNetB5, and EfficientNetB7 have obtained only lesser performance. The proposed GICANet classifier outperforms the other approaches utilizing two error metrics, RMSE (0.5) and MAE (1.5).

This paper is based on fuzzy clustering algorithm on financial data, design of financial data mining system and analysis of financial data, analysis of financial data on the financial decision-making mechanism, from financial data how to enhance the information base of the forecast, financial data how to improve the pertinence of decision-making, financial data how to build a new competitive advantage, financial data how to promote the dynamic decision-making of four, through the analysis of data in financial decision-making specific implementation cases, focusing on the real-life problems faced by the management and the effect of financial data platform to solve problems; finally, through this paper, we hope to provide reference and reference for other similar enterprises to apply financial data for financial decision-making. This paper first describes the theory of financial data, analyzes the mechanism of financial data and financial decision-making, how financial data enhance the information base of forecasting, how financial data improve the pertinence of decision-making, how financial data build a new competitive advantage, how financial data promote dynamic decision-making four dimensions to summarize.

Clustering is an important research area that has practical applications in many fields. Fuzzy clustering has shown advantages over crisp and probabilistic clustering, especially when there are significant overlaps between clusters. Most analytic fuzzy clustering approaches are derived from Bezdek's fuzzy c-means algorithm. One major factor that influences the determination of appropriate clusters in these approaches is an exponent parameter, called the fuzzifier. To our knowledge, no theoretical reason leading to an optimal setting of this parameter is available. This paper presents the development of an heuristic scheme for determining the fuzzifier. This scheme creates close interactions between the fuzzifier and the data set to be clustered. Experimental results in clustering IRIS data and in code book design required for image compression reveal a good performance of our proposal.

In this paper a novel approach is introduced for modeling and clustering gene expression time-series. The radial basis function neural networks have been used to produce a generalized and smooth characterization of the expression time-series. A co-expression coefficient is defined to evaluate the similarities of the models based on their temporal shapes and the distribution of the time points. The profiles are grouped using a fuzzy clustering algorithm incorporated with the proposed co-expression coefficient metric. The results on artificial and real data are presented to illustrate the advantages of the metric and method in grouping temporal profiles. The proposed metric has also been compared with the commonly used correlation coefficient under the same procedures and the results show that the proposed method produces better biologicaly relevant clusters.

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi–Sugeno–Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

In a complex network, the global similarity standard is of high complexity and local similarity standard has biased error on node similarity. This paper puts forward a community discovering algorithm OBS, based on the degree of contribution of shared nodes in complex network. It distributes the contribution, using the degree of shared nodes as the unit, equally, which is made by shared nodes. Compared with average commute time standard, Random Walk with Restart standard and Hierarchical Structure Method standard, OBS standard has a more veracious forecast to the similar relationship between nodes. The experimental results of artificial network and typical complex network show that OBS algorithm has a better stability than GN, Fast-Newman (FN), and N-cut algorithms.

The switching regression problems are attracting more and more attention in a variety of disciplines such as pattern recognition, economics and databases. To solve switching regression problems, many approaches have been investigated. In this paper, we present a new integrated clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton's Gravity Law. Our theoretic analysis shows that GFC can converge to a local minimum of the object function. Our experimental results illustrate that GFC for switching regression problems has better performance than standard fuzzy clustering algorithms, especially in terms of convergence speed. Hence GFC is a new more efficient algorithm for switching regression problems.

In this paper we present a voting scheme for fuzzy cluster algorithms. This voting method allows us to combine several runs of cluster algorithms resulting in a common partition. This helps us to tackle the problem of choosing the appropriate clustering method for a data set where we have no a priori information about it. We mathematically derive the algorithm from theoretical considerations. Experiments show that the voting algorithm finds structurally stable results. Several cluster validity indexes show the improvement of the voting result in comparison to simple fuzzy voting.

Although a detailed empirical analysis of the world city network is essential to attain insight in its functioning, it can be noted that previous explorations have been restricted to analyses of a limited number of thoroughly connected cities. A major reason for the neglect of less connected nodes in this global urban network is the sparse evidence on their world city formation. Drawing on earlier specifications and measurements of the world city network, the present paper shows how fuzzy set approach and pattern recognition can assess the inherent vagueness in classifications of lower ranked world cities. The resulting taxonomy asserts the intertwining relational tendencies of 234 cities in 20 clusters. Key findings include the distinctive profiles of US cities, the marginal position of (sub-Saharan) African and Central American cities, and Miami's particular role as a gateway between Anglo- and Latin America.

There are several commonly accepted clustering quality measures (clustering quality as opposed to cluster quality) such as the rand index, the adjusted rand index and the jacquard index. Each of these however is based on comparing the partition produced by the clustering process to a correct partition. They can therefore only be used to determine the quality of a clustering process when the correct partition is known. This paper therefore proposes another clustering quality measure that does not require the comparison to a correct partition.

The proposed metric is based on the assumption that the proximities between the membership vectors should correlate positively with the proximities between the objects which may be the proximities between their feature vectors. The values of the components of the membership vector, corresponding to a pattern, are the membership degrees of the pattern in the various clusters. The membership vector is just another object data vector or type of feature vector with the feature values for an object being the membership values of the object in the various clusters. Based on this premise, this paper describes some new cluster quality metrics derived from standard correlation measures and other proposed correlation metrics.

Simulations on data with a wide range of clusterability or separability show that the approach of comparing the proximity matrix based on the membership matrix to the object proximity matrix is quite effective.

Associating features with weights is a common approach in clustering algorithms and determining the weight values is crucial in generating valid partitions. In this paper, we introduce a novel method in the framework of granular computing that incorporates fuzzy sets, rough sets and shadowed sets, and calculates feature weights automatically. Experiments on synthetic and real data patterns show that our algorithms always converge and are more effective in handling overlapping among clusters and more robust in the presence of noisy data and outliers.

In predicting water quality variables in the short term, a novel technique using fuzzy pattern similarity-based fuzzy clustering has been proposed. The experimental results show that the proposed method outperforms than existing similar methods for sea water temperature and conductivity data sets from a marine sensor network for environmental monitoring. The short-term prediction of water quality variables has immense benefit in aquaculture and fisheries industries for decision-making purposes.

Brain Magnetic Resonance Imaging (MRI) image segmentation is one of the critical technologies of clinical medicine, and is the basis of three-dimensional reconstruction and downstream analysis between normal tissues and diseased tissues. However, there are various limitations in brain MRI images, such as gray irregularities, noise, and low contrast, reducing the accuracy of the brain MRI images segmentation. In this paper, we propose two optimization solutions for the fuzzy clustering algorithm based on local Gaussian probability fuzzy C-means (LGP-FCM) model and anisotropic weight fuzzy C-means (AW-FCM) model and apply it in brain MRI image segmentation. An FCM clustering algorithm is proposed based on AW-FCM. By introducing the new neighborhood weight calculation method, each point has the weight of anisotropy, effectively overcomes the influence of noise on the image segmentation. In addition, the LGP model is introduced in the objective function of fuzzy clustering, and a fuzzy clustering segmentation algorithm based on LGP-FCM is proposed. A clustering segmentation algorithm of adaptive scale fuzzy LGP model is proposed. The neighborhood scale corresponding to each pixel in the image is automatically estimated, which improves the robustness of the model and achieves the purpose of precise segmentation. Extensive experimental results demonstrate that the proposed LGP-FCM algorithm outperforms comparison algorithms in terms of sensitivity, specificity and accuracy. LGP-FCM can effectively segment the target regions from brain MRI images.

To obtain the fault features of the bearing, a method based on variational mode decomposition (VMD), singular value decomposition (SVD) is proposed for fault diagnosis by Gath–Geva (G–G) fuzzy clustering. Firstly, the original signals are decomposed into mode components by VMD accurately and adaptively, and the spatial condition matrix (SCM) can be obtained. The SCM utilized as the reconstruction matrix of SVD can inherit the time delay parameter and embedded dimension automatically, and then the first three singular values from the SCM are used as fault eigenvalues to decrease the feature dimension and improve the computational efficiency. G–G clustering, one of the unsupervised machine learning fuzzy clustering techniques, is employed to obtain the clustering centers and membership matrices under various bearing faults. Finally, Hamming approach degree between the test samples and the known cluster centers is calculated to realize the bearing fault identification. By comparing with EEMD and EMD based on a recursive decomposition algorithm, VMD adopts a novel completely nonrecursive method to avoid mode mixing and end effects. Furthermore, the IMF components calculated from VMD include large amounts of fault information. G–G clustering is not limited by the shapes, sizes and densities in comparison with other clustering methods. VMD and G–G clustering are more suitable for fault diagnosis of the bearing system, and the results of experiment and engineering analysis show that the proposed method can diagnose bearing faults accurately and effectively.

In order to realize the diagnosis of the state of the high-voltage circuit breaker in the smart grid, the wavelet-packet technique is used to extract the characteristic value of the signal of the dynamic contact of the high-voltage circuit breaker. The characteristic value of the obtained signal is processed by fuzzy clustering, which inputs the processed feature values into the Support Vector Machine (SVM) to implement fault diagnosis. The high-voltage circuit breakers that need to be identified have the following faults: contact spring failure, trip spring shaft pinout, and trip spring failure. After the above series of processes, the paper reached the conclusion that it is feasible to use SVM to diagnose the high voltage circuit breaker fault system, which has a good diagnostic effect.

Particle Swarm Optimization (PSO) is a population-based meta-heuristic known for its simplicity, being successfully used in clustering task with interesting performance. Clustering of multi-view data sets has received increasing attention since it explores multiple sources or views of data sets aiming at improving clustering accuracy. Previous studies mainly focused on PSO-based clustering of single-view vector data, neither single- nor multi-view PSO-based clustering of relational received proper attention. This paper introduces a PSO-based approach to the fuzzy clustering of multi-view relational data, which can cluster data sets described by several dissimilarity matrices, each of them representing a particular view. In this work, ten fitness functions were considered, in which eight of them were adapted to deal with multi-view relational data and to consider the relevance weights of views. These fitness functions were compared to evaluate which best fit to cluster multi-view relational data. The performance and usefulness of the proposed approach, in comparison with previous single- and multi-view relational fuzzy clustering algorithms, are illustrated with several multi-view data sets. The Adjusted Rand Index (ARI) and F-measure were used to assess the quality of fuzzy partitions provided by clustering algorithms. The results have shown that the proposed methods significantly outperformed the compared algorithms in the majority of cases.

In this paper we further explore the use of machine learning (ML) for the recognition of 3D objects in isolation or embedded in scenes. Of particular interest is the use of a recent ML technique (specifically CRG — Conditional Rule Generation) which generates descriptions of objects in terms of object parts and part-relational attribute bounds. We show how this technique can be combined with intensity-based model and scene–views to locate objects and their pose. The major contributions of this paper are: the extension of the CRG classifier to incorporate fuzzy decisions (FCRG), the application of the FCRG classifier to the problem of learning 3D objects from 2D intensity images, the study of the usefulness of sparse depth data in regards to recognition performance, and the implementation of a complete object recognition system that does not rely on perfect or synthetic data. We report a recognition rate of 80% for unseen single object scenes in a database of 18 non-trivial objects.

This paper describes a class of models we call semi-supervised clustering. Algorithms in this category are clustering methods that use information possessed by labeled training data X^d⊂ ℜ^p as well as structural information that resides in the unlabeled data X^u⊂ ℜ^p. The labels are used in conjunction with the unlabeled data to help clustering algorithms partition X^u ⊂ ℜ^p which then terminate without the capability to label other points in ℜ^p. This is very different from supervised learning, wherein the training data subsequently endow a classifier with the ability to label every point in ℜ^p. The methodology is applicable in domains such as image segmentation, where users may have a small set of labeled data, and can use it to semi-supervise classification of the remaining pixels in a single image. The model can be used with many different point prototype clustering algorithms. We illustrate how to attach it to a particular algorithm (fuzzy c-means). Then we give two numerical examples to show that it overcomes the failure of many point prototype clustering schemes when confronted with data that possess overlapping and/or non uniformly distributed clusters. Finally, the new method compares favorably to the fully supervised k nearest neighbor rule when applied to the Iris data.