A behavior cluster based availability prediction approach for nodes in distribution networks
To predict the availability state of a node in a distribution network, its history trace is usually used. Sometimes, some usage behavior patterns cannot be captured precisely from the insufficient trace, which may lead to unreliable predictors. In this paper, to alleviate the data sparseness problem, the nodes with the similar behaviors are clustered, and all history information in a same cluster is seen as another information source for any node in it. For each node, an N-gram model is used to train the predictor by the combination of the new source and the node's own trace. In addition, because it is hard to capture the trace of all nodes in large scale networks, such as P2P networks, a bagging based prediction algorithm is proposed, which can be applied in the distribution environment and relieve the effect of the noisy data. In our experiments, three datasets are evaluated. Results show that the prediction performance of our cluster based N-gram predictor is better than the results of several other predictors. And the bagging based prediction algorithm presents its validity in the distribution environment.