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First, we classify the objects in continuous domain decision table according to fuzzy clustering; then, combining rough set theory with fuzzy set theory, an attribute reduct algorithm of decision table with continuous attributes is put forward; at last, a rule extraction algorithm is proposed and also the validity of this algorithm is accounted for through an example.
Eighty eight tobacco samples from six provinces in China, of which the contents of rare earth elements (REEs) were determined by microwave digestion-inductively coupled plasma mass spectrometry method. A fuzzy clustering method, fuzzy c-means (FCM), was used for classification of the different kinds of tobaccos based on their contents of REEs. The results show that FCM clustering analysis is a valid...
Intrusion of network which couldn't be analyzed, detected and prevented may make whole network system paralyze while the abnormally detection can prevent it by detecting the known and unknown character of data. A mixed fuzzy clustering algorithm that uses Quantum-behaved Particle Swarm Optimization (QPSO) algorithm and combines with Fuzzy C-means (FCM) is adopted in this paper and used in abnormally...
In the pattern recognition subspace method, the researcher has paid more attention to extract feature subspace, then expressed individual prototype with the training sample mean. Because the number of training sample is limited, there is certain difference between the sample mean and the individual prototype. In order to reduce this difference, a sample restraint clustering algorithm was proposed,...
This paper proposes a novel vehicle detecting approach for surveillance scenes with single stationary camera. Difference accumulative based background modeling method is used for background modeling. Background subtraction operation is used for detecting moving vehicles and Otsu method is used to threshold the background difference image. Subtractive clustering algorithm is applied for vehicle locating...
A new clustering classification approach based on fuzzy closeness relationship (FCR) is studied in this paper. As we know, fuzzy clustering classification is one of important and valid methods to knowledge discovery. One of problems in fuzzy clustering classification is to determine a certain fuzzy sample classification in given limited sample space. Another is its validity, that is to say, if the...
The advantages of both grey clustering method and fuzzy ISODATA method were analyzed and colligated. First, the decision-making evaluation results of uncertainty system with small sample were acquired with grey clustering method. Then, applying the fuzzy ISODATA model to learn and revise the results of above grey clustering, an optimal fuzzy classification could be produced through iterative operation...
This paper introduces the C-means fuzzy clustering method to evaluate the road traffic status. During the analysis, road traffic status was categorized into four types by using ISODATA algorithm based on expert knowledge. Meanwhile, RBF neural network classification model was established to evaluate the road traffic status. The implementation results showed that the proposed method was capable of...
By analyzing the related theory and methods commonly used by the current retailing enterprises, the main basis and influencing factors of commodity procurement under the supply chain environment can be affirmed. As well, classification of the commodity can be carried out according to 8 factors, such as price, delivering cycle, storage life, purchase quantity, sale quantity, transportation cost, seasonality...
A hybrid constrained semi-supervised clustering algorithm(HCC) is proposed, both labeled data and pairwise constraints are concerned in clustering a given dataset to get a better clustering result. This paper gives theoretical derivation and experiments on UCI data sets, and the experiments show that the quality of clustering using two kinds of constraint information is better than only one kind of...
This paper proposes an improved FCM algorithm aiming at many problems in Fuzzy C Means algorithm, such as being sensitive to initial conditions, usually leading to local minimum results. The new algorithm can obtain global optimal solutions through a new simple and efficient selecting rule of the initial cluster centers, furthermore alternating optimization in terms of a novel separable criterion...
According to the problem that K-Means clustering algorithm fails to correctly distinguish non-convex shape clusters, computation mode of distance in the algorithm is changed and density metric mode which can reflect the characteristics of data themselves is adopted instead. In the mode, Delaunay triangulation graph which has the advantages of nearest neighbour and adjacency is introduced to compute...
With the widespread of Internet application, more and more enterprises build their Web sites and provide business information through Web pages. Web page classification could be used to assign the enterprise Web pages to one or more predefined business categories. On the purpose of Internet-based enterprises administration in E-government system, algorithms and application related to web page classification...
In the cluster-based image segmentation algorithm, the initialization was needed in FCM(fuzzy C-means) algorithm and there were lots of local minimum in the objective function, if the initialization obtained the local minimum vicinity point, it would cause a convergence to local minimum. In order to solve this problem, a global optimization search (GOS) algorithm was introduced to the FCM algorithm...
Non-negative matrix factorization (NMF) is useful in finding basis information of non-negative data. It is a new dimension reduction method. In this paper, a Group Locality Preserving Orthogonal Nonnegative Matrix Factorization (GLPONMF) is investigated. The idea is to extend the NMF method in order to extract basis vectors for each sample class and at the same time enforce the locality preserving...
Uncertainty is the intrinsic property of spatial data and one of important factors affecting the course of spatial data mining. There are diversiform forms for the essentiality and aspect of uncertainty in the spatial objects of geographic information system. Essentiality of uncertainty may consist of the components of randomicity, fuzzy, chaos, etc. And the latter, i.e. aspect of uncertainty, may...
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