  The clustering ensembles mingle numerous partitions of a specified data into
a single clustering solution. Clustering ensemble has emerged as a potent
approach for ameliorating both the forcefulness and the stability of
unsupervised classification results. One of the major problems in clustering
ensembles is to find the best consensus function. Finding final partition from
different clustering results requires skillfulness and robustness of the
classification algorithm. In addition, the major problem with the consensus
function is its sensitivity to the used data sets quality. This limitation is
due to the existence of noisy, silence or redundant data. This paper proposes a
novel consensus function of cluster ensembles based on Multilayer networks
technique and a maintenance database method. This maintenance database approach
is used in order to handle any given noisy speech and, thus, to guarantee the
quality of databases. This can generates good results and efficient data
partitions. To show its effectiveness, we support our strategy with empirical
evaluation using distorted speech from Aurora speech databases.
