Group mobility models for ad-hoc wireless networks are frequently used with the purpose of learning the pattern of mobile mult i-nodes which move together towards a co mmon destination. Though, network nodes in a mobile Ad-hoc network move in some motion patterns -called mobility models -but there is still a lack of study on the new parameters that are satisfactory for Ad-hoc network part ition pred iction. Normally, network partit ioning is considered as the origin for nu merous unexpected

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... ous disruptions in network routing and upper layer applications. Consequently, by exploit ing the group mobility pattern, we min imized the extent of such disruptions in an effective way for mobile group users. In this paper, we p roposed a new portrayal of group mobility derived fro m the existing group mobility models available in hand (as secondary data). In general, our model consists of two steps; 1) suggesting a discriminative pattern for group users, and 2) developing a group users' mobility prediction model. Moreover, in our experiments, we used a simu lation technique to imp rove the performance in terms of precision and recall parameters. In o rder to take full advantage of significance and predict ion of the mobility model, GM P (Group Mobility Pattern) and corruption factors were investigated. The results showed that our method is more accurate in making predictions compared with the two existing methods (MC/MT and TM) designated in this study.