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Initialization of fuzzy k-means algorithm decreases the convergent rate of clustering and leads to plenty of calculation. Thus, we propose an improved fuzzy k-means clustering based on k-center algorithm and binary tree in this paper, which firstly reduces redundant attributes while too many irrespective attributes affect the efficiency of clustering. Secondly, we remove the differences of units of...
In order to improve searching results of Web pages and enhancing Web crawling operation, the Web page clustering based on searching keywords is proposed in this paper, which firstly employed matching degree between Web pages and searching keywords to decide the sequence of showing pages of searching results. Then clustering algorithm was chosen to group pages of searching results according to matching...
Many clustering ensemble algorithms need to predesign initial thresholds before partition data points, which is supervised learning and directly influence the efficiency of clustering. In order to cluster data points under fully unsupervised situation, the hierarchical partition is introduced in this paper. The proposed algorithm makes use of the distribution of results of all clustering memberships...
Present, there is more research on supervised clustering ensemble algorithm, but the research on unsupervised clustering ensemble is studied less. In order to partition data points under fully unsupervised conditions, the hierarchical clustering ensemble algorithm based on association rules (HCEAR) is proposed in this paper. The optimal number of clusters is determined by average degree of clustering...
The variables of organic matter, available N, available P and available K data determined in 193 topsoil (0-30 cm) samples were selected as data sources. Fuzzy c-means clustering algorithm was used to delineate management zones. In order to determine the optimum fuzzy control parameters, the fuzziness performance index (FPI), c-?? combinations and the multiple regression based on external variable...
Existing clustering ensemble algorithms for partitioning categorical data only apply to know the generating process of clustering members very well. In order to broaden the application of clustering ensemble, a fuzzy clustering ensemble algorithm for partitioning categorical data is proposed in this paper. The proposed algorithm makes use of relationship degree between different attributes for pruning...
Ovarian cancer (OvCa) has become one of the most lethal gynecological cancers in the world. The identification of ovarian cancer linked biomarkers will provide the basis of diagnoses and treatment. In this study, we proposed to combine singular value decomposition (SVD) and Monte Carlo method to analyze the OvCa data and predict the outcomes of samples. A supervised SVD was proposed to weight biomarkers...
Web services have been considered as an effective method to create unprecedented opportunities for organizations to establish more agile and versatile collaborations with other organizations. But services dynamic discovery is one of factors not only tiring consumers but also preventing them from enjoying high quality of service. It is one of key issues in services dynamic discovery how to select the...
Traditional k-means algorithm can make the distances of objects in the same cluster as small as possible, but the distances of objects from different clusters are not satisfied efficiently and usually the dataset with mixed numeric and categorical data is not classified correctly. The IWEKM (improved weight entropy k-means) algorithm is proposed in this paper. The proposed algorithm overcomes the...
The objective of traditional k-means algorithm is to make the distances of objects in the same cluster as small as possible, but another objective that the distances of objects from different clusters is not taken into account. This paper presents an improved k-means algorithm satisfying both of objectives above. We modify the cost function of entropy weighting k-means clustering algorithm by adding...
In modern society, steel companies have played great role in economic construction. This paper makes use of fuzzy clustering approach as an important mathematic tool to classify steel companies according to some general financial indexes, such as ratio of operating income, ratio of stockholder's equity and current ratio. By constructing and normalizing initial partition matrix, getting fuzzy similar...
Biological sequence usually contains yet to find knowledge, and mining biological sequences usually involves a huge dataset and long computation time. Common tasks for biological sequence mining are pattern discovery, classification and clustering. The newly developed model, plausible neural network (PNN), provides an intuitive and unified architecture for such a large dataset analysis. This paper...
A hybrid methodology is proposed to take advantage of the unique strength of autoregressive integrated moving average (ARIMA) and RBF (radial basis function) neural networks in linear and nonlinear modeling, which is an error correction method to create synergies in the overall forecasting process. ARIMA model is used to generate a linear forecast in the first stage, and then RBFN is developed as...
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