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Uncertain data clustering is one significant research in data mining. Many similarity measurements of uncertain objects are proposed. Traditional clustering methods can be extended with these new similarity measurements. In this paper, we propose a new fuzzy c-medoids method for uncertain data clustering, named UFC-medoids. The JS-divergence is used as the similarity measurement between uncertain...
In recent years, combining multiple attributes of data sets including numerical and categorical attributes for data stream clustering has been a popular practice for improving clustering accuracy. In this paper, we propose a novel data stream clustering algorithm called WKStream which can cluster numerical and categorical data stream with different shapes efficiently. As different attributes have...
Clustering algorithm is one of the fundamental techniques in data mining, which plays a crucial role in various applications, such as pattern recognition, document retrieval, and computer vision. As so far, many effective algorithms have been proposed. Affinity Propagation is an algorithm requires no parameter indicating the number of clusters, which is the most distinguishing advantage compared to...
Uncertain data clustering is an essential task in the research of data mining. Lots of traditional clustering methods are extended with new similarity measurements to tackle this issue. Different from certain data clustering, uncertain data clustering focus more on the evaluation of distribution similarity between uncertain data objects. In this paper, based on the KL-divergence and the JS-divergence,...
Nonlinear clustering has attracted an increasing amount of attention recently. In this paper, we propose a new nonlinear clustering method based on Cluster Shrinking and Border Detection (CSBD). Unlike most existing clustering method, the CSBD method focuses on every data point rather then the cluster centers. A novel idea, namely Cluster Shrinking, is designed to transform the original nonlinear...
The original Artificial Immune Network (aiNet) clustering algorithm cannot get an ideal result when the boundaries of the dataset are not clear or the noise is present. In this paper, an improved Artificial Immune Network algorithm for data clustering based on Secondary competition (cs-aiNet) is proposed to solve this problem. The strategy named competition selection is introduced to select a node...
With the amount of data increasing rapidly, how to improve the scalability of nonlinear clustering has become a very crucial and challenging problem. In this paper, we design an efficient parallel nonlinear clustering algorithm by using a four-stage MapReduce framework. In our approach, we need to compute two quantities based on distance matrices, which, however, is difficult to compute in a MapReduce...
Multi-view clustering has become a popular clustering technique in recent years due to its ability to analyze data collected from multiple sources or represented by multiple views. In this paper, we propose a novel multi-view clustering approach termed weighted multi-view online competitive clustering (WMLCC). We simultaneously exploit the variable weighting strategy and the online competitive learning...
With the explosive growth number of services in cloud computing environment, how to accurately and rapidly discover the services that can meet user's functional and nonfunctional requirements is a challenging subject. Aiming at issues of service inefficiencies and low precision in the existing service discovery methods, a model for service discovery based on functions and QoS clustering is proposed...
Hierarchical clustering has received a great amount of attention due to the capability of capturing hierarchical cluster structure in an unsupervised way. Despite great success, most of the existing hierarchical clustering algorithms have some drawbacks: (1) difficulty in selecting clusters to merge or split, (2) inefficient and inaccurate cluster validation, (3) limitation to only linearly separable...
In wireless sensor networks, the energy of nodes is limited. so designing efficient routing for reducing energy consumption is important. In this paper we proposed A Low Power Grid-based Cluster Routing Algorithm of Wireless Sensor Networks (LPGCRA). The characteristic of this algorithm is that the WSN is divided into different grids according to information of the node location, and then the nodes...
An Iterative Clustering Steiner Tree (ICST) algorithm is proposed to connect hose based VPN endpoints using a shared tree for resource optimization. Simulation results show the ICST algorithm can achieve better performance on resource utilization.
In this paper, we put forward a model of multi-agent based on intrusion detection system for wireless sensor networks, and a new method of detection called refined clustering, which is suggested running on some agents. In this new method we use self-organizing map (SOM) neural network to cluster roughly the samples, and the next step the K-means clustering algorithm is adopted to refine the clustering...
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