Abstract

In social media, organizing friendship relationships is difficult since the more number of increasing users in Online Social Networks (OSN). To overcome this challenge, users in OSN heavily depend on grouping which is considered to be advantageous but at the same time found to be more cumbersome. More recently, recommender system and novel data clustering algorithm have been presented in OSN to address this concern. However, with the increasing nature and size of data (i.e., big data), grouping of users in OSN has become opened research for several academic’s professionals. In this paper, a novel clustering algorithm with optimized extreme machine learning which is called, Optimized Extreme Machine Learning and Orthogonally Projected (OEML-OP) is presented for user grouping in OSN to reducing computational overhead and time from large streams of social data. OEML-OP method utilizes new mechanism in EML to include optimality, a mechanism that is inspired by modularity function. The method performs user grouping through three main steps including, Duality Proportionality Graphical model, identifying optimal clusters and grouping of users in OSN. The Duality Proportionality Graphical model is to generate cluster of cliques and optimal clusters for the second steps and the Orthogonal Projection maximize the margin for separation between clusters. Due to the collective mechanism, the OEML-OP method gives better clustering accuracy and provides a novel model of grouping users on the basis of their activities. The analysis shows that the proposed method provides preferable clustering results and imparts a novel use-case of user grouping in OSN based on their activities.

中文翻译：

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在线社交网络的优化极限学习群集和正交规划的用户分组

摘要

在社交媒体中，由于在线社交网络（OSN）中越来越多的用户越来越多，因此组织友谊关系非常困难。为了克服这一挑战，OSN中的用户在很大程度上依赖于分组，这被认为是有利的，但同时又显得麻烦。最近，在OSN中已经提出了推荐系统和新颖的数据聚类算法，以解决这一问题。但是，随着数据（即大数据）的性质和大小的增长，OSN中的用户分组已成为几位学术界专业人员的公开研究。在本文中，一种具有优化的极限机器学习的新型聚类算法称为 针对OSN中的用户分组，提出了优化的极限机器学习和正交投影（OEML-OP），以减少来自大量社交数据流的计算开销和时间。OEML-OP方法利用EML中的新机制来包括最优性，该机制受模块化功能的启发。该方法通过三个主要步骤执行用户分组，包括对偶比例图形模型，识别最佳群集和OSN中的用户分组。对偶比例图形模型将为第二步生成团簇和最佳簇，而“正交投影”则使簇之间分离的余量最大化。由于采用了集体机制，OEML-OP方法具有更好的聚类精度，并提供了一种基于用户活动进行分组的新颖模型。