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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.
A generalised fuzzy approach to statistical modelling techniques for pattern recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to third-order two-dimensional hidden Markov mode(2-D HMM). 2-D HMM is an extension of 1-D HMM to 2-D, it provides a reasonable statistical method to model matrix data. By...
This paper presents a kernel-based fuzzy c-means algorithm with partition index maximization, called KPIM algorithm. The proposed KPIM algorithm is more robust than the partition index maximization algorithm proposed by Özdemir and Akarum. Experiments show that the advantage of KPIM are robust properties: (1) robust to fuzziness parameter m, (2) robust to outlier, (3) robust to image artifacts; and...
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...
Colored fibers can be blended in a certain proportion to achieve a specific color. It is a very hard task for the colorist to find a good recipe to meet the final product without the aid of computer. In this paper, a color matching method for the colored fiber blends is discussed to substitute some manual work. The fuzzy C-mean cluster way is carried to group the color in the colored fiber blends...
The fuzzy c-means algorithm is a useful technique for clustering real s-dimensional data, but it can not be directly used for partially missing data sets. In this paper, the problem of missing data handling for fuzzy clustering is considered, and a statistical representation of missing attributes is proposed. The approach reduces the statistical analysis of missing attributes to the subsets of the...
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...
Each image has its own color content that greatly influences the perception of human observer. Recently, color transfer among different images has been under investigation. In this paper, after a brief review on the few efficient works performed in the field, a novel fuzzy clustering based color transfer method is proposed. The proposed method accomplishes the transformation based on a set of corresponding...
In radar target detection application fields, rich of information of targets may be included in medium frequency. Doppler shift caused by moving target is one of them. In this paper, Doppler shift and its Short Time Fourier Transform-STFT is analyzed and discussed. Since it is difficult to distinguish the Doppler shift of moving target even in frequency domain with STFF method, fuzzy C means clustering...
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...
In order to resolve the computational complexity for local map matching of hierarchical simultaneous localization and mapping (SLAM), a novel self-organizing fuzzy neural networks (SOFNN) based approach was proposed in this paper. The matching component for local maps in the hierarchical SLAM is realized by an SOFNN. A subset of signature elements included in a local map was chosen by a clustering...
Resource management for multitarget detection in Heterogeneous Sensor Networks (HSN) is an open research area. By considering communication capabilities, energy differences and mobility dissimilarities jointly, we propose a fuzzy logic system (FLS) and apply fuzzy c-mean (FCM) clustering to adaptively select sensors that report surrounding targets information for further data fusion. Monte Carlo simulations...
Transformers fault diagnosis plays a vital role in running security and reliability. The detected information is collected from the disperse sensor, which is lack of the data fusion analysis and easily lead to decision error and leak. A model of oil-immersed transformer fault diagnosis based on the collaborative method of Kernel C-Means Clustering (KCM) and multi-source information data fusion is...
Annual runoff forecasting is very important for improvement of the management performance of water resources: high accuracy in runoff prediction can lead to more effective use of water resources. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast annual runoff of Yamadu hydrological station in Xinjiang Province, China. The subtractive...
Fuzzy theory is applied in the time-lapse seismic system, considering the fuzziness of exploration data and geological information. A new fuzzy clustering method is introduced; it requires only that the degree of fineness of clustering be set. The method provided valuable information for geological interpretation. And then how high-level of object-oriented concepts can be used to provide a generic,...
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...
On mining quantitative association rules and the segmentation of numerical attributes variables of ordered data, Fisher cluster method can be used to determine the segmentation range and the segmentation number of this variable value. The method takes the data interval and data density into account. Therefore it is of great significance to data pre-processing.
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...
Based on the complex network theory, we proposed a clustering algorithm based on content similarity. Firstly, the Chinese documents are represented by the vector-space model, and the content similarity between any two documents is computed by the cosine similarity. Consequently, the network node is defined as a document, and the edge weight is defined as the similarity obtained by the cosine similarity...
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