Nuclear Clustering Algorithm on State Grids IT Operation Log With the continuous evolution of the IT infrastructure of State Grid Data Center and the growing operational data in the electric power system, how to quickly and automatically cluster the operation log in the data center of State Grid has become a key issue in the IT operation and maintenance of the data center. As the most commonly used algorithms in data mining, a clustering algorithm from data mining is adopted to handle the operational log data of State Grid IT data center, which can be used to effectively discover the changes of the topology structure during the operation of the IT infrastructure. Specifically, because the traditional sequential clustering algorithm lacks the ability to discover potential links in logs, this paper proposes a selfdestructive nuclear clustering algorithm SDNmeans, which aims at the business and data characteristics of the IT infrastructure system of State Grid data center, in order to effectively classify the operational log data of State Grid IT data center during the operation of State Grid. Through the analysis of the running logs of State Grid data center with obvious time series characteristics, the proposed SDNmeans algorithm can effectively outperform the existing approaches on the operation of the data center of State Grid.

Energy analysis of Internet of things data mining algorithm for smart green communication networks 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 peoplexe2x80x99s 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 densitybased 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 highlevel 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.