Open AccessAn Improved Distance Metric Clustering Algorithm for Association Rules
Author(s) -
Futai Liang,
Hongquan Li,
Weifeng Zhang,
Chenhao Zhang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1284/1/012030
Subject(s) - association rule learning , data mining , apriori algorithm , cluster analysis , computer science , association (psychology) , similarity (geometry) , metric (unit) , database transaction , dbscan , set (abstract data type) , transaction data , artificial intelligence , correlation clustering , cure data clustering algorithm , image (mathematics) , engineering , database , philosophy , operations management , epistemology , programming language
By mining association rules in large data, we can reveal useful information contained in the data and find out the relationship between things or the law of motion. However, because of the huge transaction data, the association rules obtained by mining are complex and massive. It is difficult to find useful association relations, especially when the re-demand is uncertain. To solve this problem, this paper first uses Apriori algorithm to mine association rules from a data set, then defines similarity measure between association rules, and applies DBSCAN clustering algorithm to association rules analysis. The analysis results show that this method is effective in association rules analysis.