الگوریتم داده کاوی اینترنت اشیا
Abstract
۱٫ Introduction
۲٫ Model design of data processing platform based on Cloud Computing
۳٫ Implementation and energy analysis of internet of things data mining algorithms on cloud platform
۴٫ Experiments and results
۵٫ Conclusions
Declaration of Competing Interest
References
Abstract
With the continuous development of Internet technology and electronic information technology, big data technology and cloud computing technology also rise and develop, and have a positive impact on people’s lives. Data mining system can deeply mine the value information contained in big data, so as to assist users to solve practical problems and help users to make correct decisions and judgments. This paper presents an energy analysis of data mining algorithm based on cloud platform for Internet of things (IoT). First of all, an improved Apriori algorithm is proposed, which is based on Boolean matrix and sorting index rules. Then Boolean matrix is obtained after scanning the dataset and the Boolean matrix is preprocessed to delete the useless transactions and the item set, which are combined with sorting index to produce other item sets, effectively improving the efficiency of frequent item mining, which effectively reduce the memory usage. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm needs human intervention in the global parameter selection, and the process of regional query is complex and the query is easy to lose objects. An improved parameter adaptive and regional query density clustering algorithm is proposed, which can effectively delete the redundant data in the high-level complex data space on the premise of retaining the internal nonlinear structure of the IoT data. The efficiency of clustering is also improved accordingly. Finally, the simulation based on cloud platform verifies the effectiveness and superiority of the algorithm.
Introduction
With the continuous development of Internet and the intelligent terminals, the amount of data has become more and more, and the types of data have become extremely rich, which represents that human beings have been big data age [1]. In recent years, data analysis of the Internet of things (IoT) has attracted more and more [2]. Through effective data analysis methods, the characteristics and rules of the Internet of things can be mined to reduce the loss in each life cycle of the Internet of things [3]. At present, the traditional Internet of things data analysis method needs to establish a high-precision mathematical model of the IoT data system, and the system is limited to the linear and time invariant system [4]. At the same time, it has high requirements for knowledge in the professional field and low precision shortcomings, so it has great limitations in application. At present, the collection mechanism of Internet of things data in many areas is becoming more and more perfect [5]. The Internet of things database will collect Internet of things data, which contains a lot of undiscovered massages, and the Internet of things data has many disadvantages, but it is difficult for traditional Internet of things data analysis methods to find and summarize the content of these Internet of things data [6]. Knowledge discovery, machine learning and data mining technology are just new disciplines born under this background. Because data mining technology has the ability to deal with large amount of data and find potential massages, it is a way to mine important massages in IoT data [7].