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Aiming at the knowledge mining from fuzzy and uncertain information, the definition mode and properties of the fuzzy formal context are discussed in the paper. The method of constructing the fuzzy concept lattice of the fuzzy formal context is proposed, in that the definition of fuzzy product concept is the core: the intents of two concepts are combined to form the intent of the product concept; using...
To improve the intelligibility and efficiency of knowledge expression for the land evaluation, a land evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the land evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate...
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...
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...
Aiming to these presented frequent neighboring class set mining algorithms have some repeated computing and redundancy candidate frequent neighboring class set when these algorithms extract frequent neighboring class set, this paper proposes an algorithm of mining frequent neighboring class set based on increasing sequence, which is suitable for mining frequent neighboring class set of objects in...
As an important preprocessing technology in patent knowledge utilization, patent classification should be accurate and efficient. Commonly used feature selection methods and classification algorithms, like information gain (IG) and k nearest neighbors (k-NN) algorithm, are superior in text classification but have some drawbacks in patent classification. In the paper, we focus on patent classification...
Text Categorization (TC) is an important component in many information organization and information management tasks. In many TC applications, the case-base grows at a fast rate and this causes inefficiency in the case retrieval process. Using Case-Base Maintenance learning via the GC (Generalization Capability) algorithm, which can reduce the case number into KNN algorithm, can improve efficiency...
In the field of imbalance learning and cost sensitive learning, minimization of the classification error rate is not an appropriate approach due to class skew and cost distributions. Thus the area under the ROC Curve (AUC) has been widely utilized to assess the performance of the classifiers in such cases. The Maximum AUC Linear Classifier (MALC), aiming at maximizing AUC directly, is a nonparametric...
A Multi-relational Bayesian Classification Algorithm with Rough Set is proposed in this paper. The concept of relational graph used to dynamic choice associative table associated with the target table, and a tuple ID propagation approach is used to solve directly the association rule mining problem with multiple database relations, and the concept of Core in Rough Set is introduced, simplify the associative...
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...
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...
Due to the explosive issue of attributes combination, the minimum reduction of decision-making tables is the NP-hard issue. But it is convenient and fast to decide the reduction scope by fractal dimension. The paper discusses the relationship between inherent dimension and the fractal dimension, explains the fractal dimension of one data set reflects the inherent characteristics of the data, and proposes...
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...
There are lots of ranking algorithms used in Web information retrieval. However, current algorithms have some problems: these algorithms are based on different calculation formulas to calculate the documents and query similarity or train a lot of training data to get corresponding calculation formula which calculate documents and query similarity. We know that this process is a very complex, and sometimes...
The popularity of SVMs has grown tremendously in the last few years for many different classification problems due to its generalization properties, however training SVMs require high computational power. Platt's SMO is one the fastest algorithm for training support vector machines, which takes the decomposition technique to the extreme by selecting a set of only two points as the working set then...
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