Early warning of enterprise finance risk of big data mining in internet of things based on fuzzy association rules

2020 ◽  
Author(s):  
Hongyu Shang ◽  
Duan Lu ◽  
Qingyuan Zhou
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
Association Rules ◽  
Early Warning ◽  
Fuzzy Association Rules ◽  
Big Data Mining ◽  
Finance Risk

Research of Association Rules Algorithm Based on Matrix under Cloud Computing

2014 ◽  
Vol 568-570 ◽  
pp. 798-801
Author(s):  
Ye Qing Xiong ◽  
Shu Dong Zhang
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Computing Technology ◽  
Binary Matrix ◽  
Transaction Data ◽  
Big Data Mining ◽  
Parallel Mining

It occurs time and space performance bottlenecks when traditional association rules algorithms are used to big data mining. This paper proposes a parallel algorithm based on matrix under cloud computing to improve Apriori algorithm. The algorithm uses binary matrix to store transaction data, uses matrix "and" operation to replace the connection between itemsets and combines cloud computing technology to implement the parallel mining for frequent itemsets. Under different conditions, the simulation shows it improves the efficiency, solves the performance bottleneck problem and can be widely used in big data mining with strong scalability and stability.

Cloud Computing and Big Data

2014 ◽  
pp. 118-132
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Association Rules ◽  
Big Data Analysis ◽  
Center Problem ◽  
Privacy Concerns ◽  
Numerical Examples ◽  
Big Data Mining

This chapter aims at exploring the intersection of cloud computing with big data. The big data analysis, mining, and privacy concerns are discussed. First, this chapter deals with the software framework, MapReduce™ that is commonly used for performing Big Data Analysis in the clouds. In addition, some of the most used techniques for performing Big Data Mining are detailed. For instance, Clustering, Co-Clustering, and Association Rules are described in detail. In particular, the k-center problem is described while with reference to the association rules beyond the basic definitions, the Apriori Algorithm is outlined and illustrated by some numerical examples. These techniques are also described with reference to their versions based on MapReduce. Finally, the description of some real applications conclude the chapter.

Study the Personal Push Service of University Library Based on Big Data Mining

2014 ◽  
Vol 998-999 ◽  
pp. 1261-1265 ◽  
Author(s):  
Cheng Yi ◽  
Ying Xia ◽  
Zhi Yong Zhang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Association Rules ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Data Resource ◽  
Big Data Mining ◽  
University Library ◽  
Library Resources ◽  
Classical Association

It expounds the big data and the relevant theoretical knowledge of big data mining, In view of the lack of effective analysis of the data resource access in delivery service of university library, this paper designs the personalized recommendation system service model of university library, with clustering analysis and association rules theory as the foundation of technology. And it introduces in detail how to cluster according to the user's attribute characteristics and how to introduce minimum support to opti-mize on the basis of the classical association rules algorithm. Experiments show that the improved algorithm can improves the utilization of library resources.

scholarly journals Protecting big data mining association rules using fuzzy system

2019 ◽  
Vol 17 (6) ◽  
pp. 3057
Author(s):  
Gandikota Ramu ◽  
M Soumy ◽  
Appawala Jayanthi ◽  
J. Somasekar ◽  
K. K. Baseer
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Association Rules ◽  
Fuzzy System ◽  
Big Data Mining ◽  
Mining Association Rules
Sign in / Sign up

Export Citation Format

Share Document