面向物联网的分类研究

摘要：物联网的商业价值的提升，大量的数据随之生成，数据挖掘算法也被应用于在数据中提取潜藏的信息。在这个文章中，我将会用一个系统的方法，从知识角度，技术角度和应用角度，包括分类，聚类和关联分析，时间序列分析和异常分析，去重新认识挖掘。根据最近的应用的调研情况，随着越来越多的设备和物联网相连，大量的数据应该被分析，最新的算法也应该随着大数据的到来而修改。我们回顾这些算法，讨论面临的挑战并公开调研问题。最后应该提出一种建议性的大数据挖掘系统。47089

物联网和与其相关的技术能够和经典的网络通过网络仪器和设备无缝的衔接。物联网自从它出现起就扮演着一个至关重要的角色，涵盖了从传统的设备到一般的家庭对象，近年来吸引了学术界，工业和政府的研究者的注意力。有关于此有个很好的观点就是所有的事物都能被简单的控制和监控，能被其他物体自动识别，能够通过网络相互交流，甚至都能事物自己做一些决定。为了使物联网更智能，大量的分析技术被引进物联网中：最有价值的一个技术就是数据挖掘。

毕业论文关键词：物联网；数据挖掘；KNN算法；决策树算法

Classification of Intenet of the things

Abstract: The massive data generated by the Internet ofhings (IoT) are considered of high business value, and datamining algorithms can be applied to IoT to extract hidden information fromdata. In this paper, we give a systematic way to review datamining in knowledge view, technique view, and application view, including classiication, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. Asmore andmore devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modiied to apply to big data.We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

The Internet of Things (IoT) and its relevant technologies can seamlessly integrate classical networks with networked instruments and devices. IoT has been playing an essential role ever since it appeared, which covers from traditional equipment to general household objects and has been attracting the attention of researchers from academia, industry, and government in recent years. There is a great vision that all things can be easily controlled and monitored, can be identified automatically by other things, can communicate with each other through internet, and can even make decisions by themselves. In order to make IoT smarter, lots of analysis technologies are introduced into IoT; one of the most valuable technologies is data mining.

Keywords: IOT；Data Mining; KNN algorithm;Decision tree

摘要

Abstract iii

目录 v

1 绪论 1

1.1 物理网的研究的意义 1

1.2 数据挖掘的介绍 1

1.3 数据挖掘方面的研究进展 3

1.4 物联网中的数据挖掘的发展 4

1.5 常用分类算法的简单介绍 5

1.5.1 决策树归纳算法：面向物联网的分类研究:http://www.751com.cn/jisuanji/lunwen_49028.html