Mining multilevel association rules with dynamic concept hierarchy

2008 ◽  
Author(s):  
Yin-bo Wan ◽  
Yong Liang ◽  
Li-ya Ding
Keyword(s):  
Association Rules ◽  
Concept Hierarchy ◽  
Multilevel Association Rules

scholarly journals Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Yang Ou ◽  
Zheng Jiang Liu ◽  
Hamid Reza Karimi ◽  
Ying Tian
Keyword(s):  
Genetic Algorithm ◽  
Resource Management ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Immune Genetic Algorithm ◽  
Coding Scheme ◽  
The Relationship ◽  
Multilevel Association Rules

This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM) which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.

Discovering Personalized Novel Knowledge from Text

2010 ◽  
pp. 301-313
Author(s):  
Yi-fang Brook Wu ◽  
Xin Chen
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Noun Phrases ◽  
Background Knowledge ◽  
Semantic Distance ◽  
Concept Hierarchy ◽  
Better Than

This chapter presents a methodology for personalized knowledge discovery from text. Traditionally, problems with text mining are numerous rules derived and many already known to the user. Our proposed algorithm derives user’s background knowledge from a set of documents provided by the user, and exploits such knowledge in the process of knowledge discovery from text. Keywords are extracted from background documents and clustered into a concept hierarchy that captures the semantic usage of keywords and their relationships in the background documents. Target documents are retrieved by selecting documents that are relevant to the user’s background. Association rules are discovered among noun phrases extracted from target documents. Novelty of an association rule is defined as the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment shows that our novelty measure performs better than support and confidence in identifying novel knowledge.

scholarly journals Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules

2018 ◽  
Vol 8 (6) ◽  
pp. 4568
Author(s):  
R. Vijaya Prakash ◽  
S. S. V. N. Sarma ◽  
M. Sheshikala
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Huge Number ◽  
Concise Representation ◽  
Multi Level ◽  
The Given ◽  
Multilevel Association Rules

Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.

Mining association rules for concept hierarchy in large database

2018 ◽  
Vol 21 (2) ◽  
pp. 457-467
Author(s):  
Chien-Hua Wang ◽  
Wei-Hsuan Lee ◽  
Chin-Tzong Pang
Keyword(s):  
Association Rules ◽  
Large Database ◽  
Concept Hierarchy ◽  
Mining Association Rules
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