Distributed clustering method of P2P (peer-to-peer) network based on believable radius of nodes

Abstract

The invention discloses a distributed clustering method of a P2P (peer-to-peer) network based on believable radius of nodes, which is characterized in that a distributed K-Means clustering algorithm is adopted by using a Fisher discrimination method to determine believable radius of clusters so as to carry out autonomic learning on data of each P2P node; nodes in the network respectively apply Fisher linear discriminant rate to determine dense and sparse distribution of the same type of data on the nodes, thereby determining the believable radius of the cluster and guiding the next cluster; and according to data distribution of each node in the network, the believable radius are calculated dynamically, thereby determining the believable radius of the clusters of each node and guiding the next cluster and the iterative process of the cluster. By using the distributed clustering method, at the same time of ensuring the clustering effects, the iterations of the clusters in the distributed network can be decreased so as to save bandwidth, and the application level of the network is improved.

Description

technical field [0001] The invention relates to the fields of computer network communication, data mining and distributed clustering, in particular to a P2P network (peer-to-peer network) distributed clustering method. Background technique [0002] Clustering analysis refers to the process of dividing a given set of data objects into multiple categories according to certain rules. Clustering makes the data objects in the same cluster as similar as possible to each other, and the data objects in different clusters Data objects are as distinct from each other as possible. So far, many clustering methods have been proposed, such as K-Means, DBSCAN, Cure, Birch, etc. Among many clustering methods, K-Means is one of the most widely used methods, because compared with other methods, K-Means method has the characteristics of simple method, easy implementation and stable clustering effect. [0003] The analysis and processing of the traditional K-Means clustering method is based o...

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Problems solved by technology

[0004]In the distributed clustering method in the P2P environment, Eisenhardt et al. proposed a distributed clustering method of Probe / Echo (detection / response) mechanism, which The method achieves a more accurate clustering effect by synchronizing node clustering in the network, but the defect of this method is that the network bandwidth usage rate is high and the bandwidth consumption is very large

Jin et al. proposed the DFEKM method, which completes clustering by calculating the confidence radius of clustering, but this method does not propose a suitable confidence radius calculation method, and uses a fixed threshold parameter for all nodes to determine the size of the confidence radius. Since clustering can only be performed locally on distributed nodes, the fixed radius set by this method does not consider the characteristics of data distribution and locality on nodes, and still requires more iterations to complete clustering

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