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Social networks can be extracted from different data about communication or common activities in organizations, companies or various Internet-based services. Different types of data processed may result in creation of separate layers in the complex multilayered social network. Analysis of neighbourhoods of network members and their utilization to social group discovery appears to be an interesting and important research domain. Since there is no measure to evaluate structure of the neighbourhoods in the multilayered social network, a new measure called cross layered multi-layered clustering coefficient (CLMCC) is proposed in the paper. It enables to analyse the density of mutual connections of neighbours that occur in at least a given number of layers in a social network. Additionally, experimental studies on real world data are presented.