Intelligent Web-History Based on a Hybrid Clustering Algorithm for Future-Internet Systems

A. Marin, Florin Pop
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引用次数: 6

Abstract

Internet grows larger year by year and makes users to be confronted with large quantities of data that they cannot fully comprehend. The ongoing transition from Web 2.0 to the Semantic Web makes the development of intelligent services with the ability to discern, classify and simplify web information of vital importance. In this paper we present a new model for web-history organizing in order to improve the user action over the Internet. Based on this model we proposed an application, delivered as a Google Chrome browser extension, which organizes the web-history into semantic clusters, providing the user with an easy-to-follow hierarchal structure. The paper covers the main algorithms in the field, offering a comprehensive critical analysis, such as document vectorization, relational clustering, fuzzy and genetic variations and the item-set-based approach. Our work consists of adapting these algorithms to support an ever-increasing set of input data. The result is a hybrid variation that rapidly offers an acceptable solution, which is optimized in time, a quality preserved during the extensive web explorations a user may undergo. A variety of test results is presented in the end, with under-stress behavior and a selection of user experience.
未来互联网系统中基于混合聚类算法的智能web历史
互联网的规模逐年扩大,用户面临着大量无法完全理解的数据。正在进行的从Web 2.0到语义Web的过渡使得智能服务的发展具有识别、分类和简化至关重要的Web信息的能力。本文提出了一种新的网络历史组织模型,以改善用户在互联网上的行为。基于这个模型,我们提出了一个应用程序,作为Google Chrome浏览器扩展交付,它将网络历史组织成语义集群,为用户提供一个易于遵循的层次结构。本文涵盖了该领域的主要算法,并提供了全面的批判性分析,如文档矢量化,关系聚类,模糊和遗传变异以及基于项集的方法。我们的工作包括调整这些算法以支持不断增加的输入数据集。结果是一个混合的变化,迅速提供了一个可接受的解决方案,它是及时优化的,在用户可能经历的广泛的网络探索中保持质量。最后给出了各种测试结果,并给出了压力下的行为和用户体验的选择。
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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