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The aim of the study is to maximize class purity level of dynamic network structure using the Supervised Parametric Clustering (SPC) approach. An efficient means to ensure proper flow sequence on heterogeneous network is the effective analysis of network traffic and its smooth maintenance. Several works in the literature have focused on access logs for analyzing network traffic, but investigating supervised clustering model for network analysis has received relatively less attention. On the other hand, Peer-to-Peer Document Clustering maintain privacy inside the neighborhood data boundaries, but fails on extending dynamic structure that reduces the high class purity mapping. To maximize the class purity level of mapping, Supervised Parametric Clustering (SPC) approach is proposed in this study. SPC approach's initial work is to analyze the flow sequences of packets in the heterogeneous network. During analyzing the data traffic flow, to ensure smoothening of network data flow, a method called binning is designed that smoothen the network data flow information discussing with neighborhood value range. Secondly, traffic data feature selection uses the Candy Best First wrapper technique for balancing the heterogeneous network traffic. The Candy Best First wrapper technique extracts the content to the particular selected feature source and translated into the relational form. Then, SPC follows the separation of medoids value points to cluster the collaborative classes of traffic data points. Finally, mapping of the SPC resultant data clusters is carried out to effectively analyze and control the traffic flow in the heterogeneous network. Experiment is carried out on the factors such as performance speedup rate, false positive rate and class purity mapping rate.

Traffic Analyzing and Controlling using Supervised Parametric Clustering in Heterogeneous Network