Association rules redundancy processing algorithm based on hypergraph in data mining

被引:0
作者
Maozhu Jin
Hua Wang
Qian Zhang
机构
[1] Business School of Sichuan University,Economic and Management School
[2] Chengdu Agricultural College,undefined
来源
Cluster Computing | 2019年 / 22卷
关键词
Data mining; Hypergraph; Association rules; Redundant processing; Smart economy; Business intelligence;
D O I
暂无
中图分类号
学科分类号
摘要
In order to achieve the research from individual data to data system and from passive verification of data to active discovery, taking high dimensional data oriented data mining technology as the research object, an association rule redundancy processing algorithm based on hypergraph in data mining technology is studied according to the project requirements. The concepts of hypergraph and system are introduced to explore the construction of hypergraph on 3D matrix model. In view of the characteristics of big data, a new method of super edge definition is adopted, which improves the ability of dealing with problems. In the association rules redundancy and loop detection based on directed hypergraph, the association rules are transformed into directed hypergraph, and the adjacency matrix is redefined. The detection of redundancy and loop is transformed into the processing of connected blocks and circles in hypergraph, which provides a new idea and method for the redundant processing of association rules. The new method is applied to the data processing of practical projects. The experimental results show that the 3D matrix mathematical model and related data mining algorithms in this paper can find new high-quality knowledge from high-dimensional data.
引用
收藏
页码:8089 / 8098
页数:9
相关论文
共 78 条
  • [1] Li J(2017)Computation partitioning for mobile cloud computing in a big data environment IEEE Trans. Ind. Inf. 13 2009-2018
  • [2] Huang L(2016)Big data meet green challenges: big data toward green applications IEEE Syst. J. 10 888-900
  • [3] Zhou Y(2016)Big data meet green challenges: greening big data IEEE Syst. J. 10 873-887
  • [4] Wu JS(2018)Imperfect Information Dynamic Stackelberg Game Based Resource Allocation Using Hidden Markov for Cloud Computing IEEE Transactions on Services Computing 11 78-89
  • [5] Guo S(2015)A structured view on pattern mining-based biclustering Pattern Recognit. 48 3941-3958
  • [6] Li J(2015)Spatiotemporal data mining: a computational perspective ISPRS Int. J. Geo-Inf. 4 2306-2338
  • [7] Wu JS(2015)Mining multidimensional contextual outliers from categorical relational data Intell. Data Anal. 19 1171-1192
  • [8] Guo S(2017)FiDoop-DP: data partitioning in frequent itemset mining on hadoop clusters IEEE Trans. Parallel Distrib. Syst. 28 101-114
  • [9] Li J(2017)Industrial Internet: a survey on the enabling technologies, applications, and challenges IEEE Commun. Surv. Tutor. 19 1504-1526
  • [10] Wei Wei(2016)Accurate RFID localization algorithm with particle swarm optimization based on reference tags J. Intell. Fuzzy Syst. 31 2697-2706