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When clustering incomplete datasets, data on cluster border (border data) are more likely to be misclassified. Aiming at this problem, the proposed algorithm focuses on the re-classification of “suspected misclassified” border data (abbreviated as SM border data). Based on the preliminary clustering results of classical FCM-based algorithm for incomplete data and the KNN (k nearest neighbor) principle,...
Cloud computing has emerged as a powerful and elastic platform for internet service hosting, yet it also draws concerns of the unpredictable performance of cloud-based services due to network congestion. To offer predictable performance, the virtual cluster abstraction of cloud services has been proposed, which enables allocation and performance isolation regarding both computing resources and network...
The goal of this research is to develop novel cluster analysis techniques to identify similarity between ICU time-series data. The results generated by cluster analysis are further used for ICU mortality prediction. To preprocess multi-granular ICU time-series, we proposed a segmentation-based method to divide time-series into several segments. The minimal and maximal values within each segment were...
Although researches on network traffic identification have already got some achievements, but most of them are not suitable for online traffic classification by considered the dynamic feature of flows. In this paper, we propose a dynamic online traffic identification method by introducing density-based clustering algorithm for stream data called DStream, and using the feature select algorithm to reduce...
The generalization ability of neural network is an important aspect affecting its application. Meanwhile, the selection of training samples has a great impact on this ability. In order to improve the completeness of training samples, a method of samples self-learning of BP neural network based on clustering is put forward in this paper. By using the method of clustering, new samples can be collected...
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...
A major problem with text classification problems is the high dimensionality of the feature space. This paper investigates how genetic algorithm and k-means algorithm can help select relevant features in text classification. which uses the genetic algorithm (GA) optimization features to implement global searching, and uses k-means algorithm to selection operation to control the scope of the search,...
The ReliefF algorithm is an important attribute weighting approach, which is built on the basis of classification labels. And the attribute weights of weighted FCM (WFCM), a popular fuzzy clustering algorithm, can be gotten by ReliefF. In the light of the idea of collaborative learning, a collaborative optimization of clustering by fuzzy c-means and weight determination by ReliefF (Co-WFCM) is introduced...
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