The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The grey relational analysis is widely used in many fields, such as education, decision-making in economics, marketing research, medicine, computer science, system modeling, social science, chemistry, management, etc. In this paper, the algorithms between grey relational analysis and fuzzy c-mean are compared. Finally, one real data set was applied to prove that the performance of the Grey Relational...
This paper presents a framework, based on Petri net and dynamic fuzzy clustering, to describe and to schedule task workflows in Grid environments. Computational Grids are intrinsically heterogeneous and dynamic systems, these features make difficult to build a resource scheduler that update itself automatically, following the resource evolution. In order to build a Grid scheduling system that automatically...
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global...
Vendor selection is a strategic issue in supply chain management for any organization to identify the right supplier. Such selection in most cases is based on the analysis of some specific criteria. Most of the researches so far concentrate on multi-criteria decision-making analysis. Though many approaches have been proposed, analytic hierarchy process (AHP) is the most well known as it can deal with...
The typical algorithm of text clustering is a ldquoHard Partitionrdquo one, Actually, Chinese text is better to treat with ldquoSoft Partitionrdquo for its diversity and largeness. The fuzzy-set theory supply a powerful analyzing tool to this ldquoSoft partitionrdquo. Traditional fuzzy text clustering methods mostly are getting the fuzzy equivalent matrix or fuzzy division by iterating the matrix...
This paper introduces the radio frequency identification (RFID) technology and the RFID anti-collision algorithms. The application mode of the RFID dynamic grouping anti-collision algorithm based on fuzzy c-means (FCM) clustering theory is stated. The selection of RFID tag grouping indexes is presented. The steps of the proposed anti-collision algorithm are developed. The simulation results show the...
Based on a distance of kernel method, a novel noise-resistant fuzzy clustering algorithm called kernel noise clustering (KNC) algorithm, is proposed. KNC is an extension of the noise clustering (NC) algorithm proposed by Dave. By replacing the Euclidean distance used in the objective function of NC algorithm, a new distance is introduced in NC algorithm. The distance of the kernel method is more robust...
Modeling and recognition of human behavior patterns for proactive service system are known to be difficult. For this purpose, an agglomerative clustering-based fuzzy-state Q-learning algorithm is suggested. In the first step of the proposed method, a meaningful structure of data is discovered by using Agglomerative Iterative Bayesian Fuzzy Clustering (AIBFC). Next in the second step, the sequence...
Clustering Web session is an important aspect of Web usage mining. In this paper, we propose a new algorithm of Web session fuzzy clustering, which applies the t-bridge algorithm to fuzzy equivalence matrix clustering algorithm. This algorithm is proved to have better accuracy, fewer CPU time and better scalability than others by the experiments.
This paper studies the problem of weighting and selecting attributes and principal axes in fuzzy clustering. Its main contribution is a selection method that is not based on simply applying a threshold to computed feature weights, but directly assigns zero weights to features that are not informative enough. This has the important advantage that the clustering result that can be obtained on the selected...
A natural Euclidean space is defined on a set of texts as sequences or hierarchical structures. Unlike the traditional term-document model, the present model takes local topological structure of texts. Kernel functions are defined that enable the use of Euclidean spaces and hence methods of data analysis based on kernels are applicable to the present model. Applications include agglomerative as well...
Many important applications in biology have underlying datasets that are relational, that is, only the (dis)similarity between biological objects (amino acid sequences, gene expression profiles, etc.) is known and not their feature values in some feature space. Examples of such relational datasets are the gene similarity matrices obtained from BLAST, gene expression data, or gene ontology (GO) similarity...
In this paper, two fuzzy classification functions of fuzzy c-means for data with tolerance are proposed. First, two clustering algorithms for data with tolerance are introduced. One is based on the standard method and the other is on the entropy-based one. Second, the fuzzy classification function for fuzzy c-means without tolerance is discussed as the solution of a certain optimization problem. Third,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.