Efficient approach for mining top-k strong patterns in Social Network Service

Brijesh Bakariya, Kapil Chaturvedi, Krishna Pratap Singh, G. Thakur
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引用次数: 2

Abstract

Social Network Service is a one of the service where people may communicate with one another; and may also exchange messages even of any type of audio or video communication. Social Network Service as name suggests a type of network. Such type of web application plays a dominant role in internet technology. In such type of online community, people may share their common interest. Facebook LinkedIn, Orkut and many more are the Social Network Service and it is good medium of making link with people having unique or common interest and goals. But the problem of privacy protection is a big issue in today's world. As social networking sites allows anonymous users to share information of other stuffs. Due to which cyber crime is also increasing to a rapid extent. In this article, we have proposed an algorithm named MSPSN (Mining Strong Pattern from Social Network). In MSPSN, considering three parameters such as User (U), Time (T) and Image (I) from weblog of Social Network Services. This algorithm is very useful to identify user behaviour in social networking service environment. Most frequently used pattern can be identify using these parameters in Social Networking Services.
社交网络服务中top-k强模式的有效挖掘方法
社交网络服务是一种人们可以相互交流的服务;也可以交换信息,甚至是任何类型的音频或视频通信。社交网络服务顾名思义是一种网络。这种类型的web应用程序在互联网技术中占据主导地位。在这种类型的在线社区中,人们可以分享他们的共同兴趣。Facebook、LinkedIn、Orkut等都是社交网络服务,它是与拥有独特或共同兴趣和目标的人建立联系的好媒介。但在当今世界,隐私保护问题是一个大问题。因为社交网站允许匿名用户分享其他东西的信息。因此,网络犯罪也在迅速增加。在本文中,我们提出了一种名为MSPSN(从社交网络中挖掘强模式)的算法。在MSPSN中,考虑来自社交网络服务博客的用户(U)、时间(T)和图像(I)三个参数。该算法对社交网络服务环境下的用户行为识别非常有用。使用Social Networking Services中的这些参数可以识别最常用的模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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