Immune clone algorithm for mining association rules on dynamic databases

2005 ◽  
Author(s):  
Hongwei Mo ◽  
Lifang Xu
Keyword(s):  
Association Rules ◽  
Dynamic Databases ◽  
Mining Association Rules

Evolutionary Approach for Mining Association Rules on Dynamic Databases

2003 ◽  
pp. 325-336 ◽  
Author(s):  
P. Deepa Shenoy ◽  
K. G. Srinivasa ◽  
K. R. Venugopal ◽  
L. M. Patnaik
Keyword(s):  
Association Rules ◽  
Evolutionary Approach ◽  
Dynamic Databases ◽  
Mining Association Rules

Approach for mining association rules based on rough set

2010 ◽  
Vol 30 (1) ◽  
pp. 25-28 ◽  
Author(s):  
Chao-bo HE ◽  
Qi-mai CHEN
Keyword(s):  
Association Rules ◽  
Rough Set ◽  
Mining Association Rules

An improved approach for mining association rules in parallel using Spark Streaming

2021 ◽  
Author(s):  
Longtao Liu ◽  
Jiabao Wen ◽  
Zexun Zheng ◽  
Hansong Su
Keyword(s):  
Association Rules ◽  
Mining Association Rules

Mining association rules between low-level image features and high-level concepts

2001 ◽  
Author(s):  
Ishwar K. Sethi ◽  
Ioana L. Coman ◽  
Daniela Stan
Keyword(s):  
Association Rules ◽  
Image Features ◽  
Low Level ◽  
Mining Association Rules ◽  
High Level

IMIDB: An Algorithm for Indexed Mining of Incremental Databases

2017 ◽  
Vol 26 (1) ◽  
pp. 69-85
Author(s):  
Mohammed M. Fouad ◽  
Mostafa G.M. Mostafa ◽  
Abdulfattah S. Mashat ◽  
Tarek F. Gharib
Keyword(s):  
Data Structure ◽  
Association Rules ◽  
Efficient Algorithm ◽  
Performance Comparison ◽  
Incremental Mining ◽  
Itemset Mining ◽  
Large Databases ◽  
Transactional Databases ◽  
Dynamic Databases ◽  
Database Size

AbstractAssociation rules provide important knowledge that can be extracted from transactional databases. Owing to the massive exchange of information nowadays, databases become dynamic and change rapidly and periodically: new transactions are added to the database and/or old transactions are updated or removed from the database. Incremental mining was introduced to overcome the problem of maintaining previously generated association rules in dynamic databases. In this paper, we propose an efficient algorithm (IMIDB) for incremental itemset mining in large databases. The algorithm utilizes the trie data structure for indexing dynamic database transactions. Performance comparison of the proposed algorithm to recently cited algorithms shows that a significant improvement of about two orders of magnitude is achieved by our algorithm. Also, the proposed algorithm exhibits linear scalability with respect to database size.

scholarly journals A Method of Mining Association Rules for Geographical Points of Interest

2018 ◽  
Vol 7 (4) ◽  
pp. 146
Author(s):  
Shiwei Lian ◽  
Jinning Gao ◽  
Hongwei Li
Keyword(s):  
Association Rules ◽  
Points Of Interest ◽  
Mining Association Rules

Mining association rules based on an improved Apriori Algorithm

2010 ◽  
Author(s):  
Yanfei Zhou ◽  
Wanggen Wan ◽  
Junwei Liu ◽  
Long Cai
Keyword(s):  
Association Rules ◽  
Apriori Algorithm ◽  
Mining Association Rules
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