Effect of Similar Behaving Attributes in Mining of Fuzzy Association Rules in the Large Databases

2006 ◽  
pp. 1100-1109 ◽  
Author(s):  
Zahra Farzanyar ◽  
Mohammadreza Kangavari ◽  
Sattar Hashemi
Keyword(s):  
Association Rules ◽  
Fuzzy Association Rules ◽  
Large Databases

Mining Association Rules

2010 ◽  
pp. 647-673 ◽  
Author(s):  
Mihai Gabroveanu
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Knowledge Discovery In Databases ◽  
Discovery Process ◽  
Fuzzy Association Rules ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Basic Concepts

During the last years the amount of data stored in databases has grown very fast. Data mining, also known as knowledge discovery in databases, represents the discovery process of potentially useful hidden knowledge or relations among data from large databases. An important task in the data mining process is the discovery of the association rules. An association rule describes an interesting relationship between different attributes. There are different kinds of association rules: Boolean (crisp) association rules, quantitative association rules, fuzzy association rules, etc. In this chapter, we present the basic concepts of Boolean and the fuzzy association rules, and describe the methods used to discover the association rules by presenting the most important algorithms.

Fuzzy Association Rules to Summarise Multiple Taxonomies in Large Databases

2010 ◽  
pp. 273-301 ◽  
Author(s):  
Trevor Martin ◽  
Yun Shen
Keyword(s):  
Set Theory ◽  
Association Rules ◽  
Fuzzy Set Theory ◽  
Large Datasets ◽  
Fuzzy Association Rules ◽  
Natural Approach ◽  
Novel Approach ◽  
Large Databases ◽  
Human Interpretation ◽  
Confidence Value

When working with large datasets, a natural approach is to group similar items into categories (or sets) and summarise the data in terms of such categories. Fuzzy set theory allows us to represent and reason about sets of objects without providing crisp definitions for each group, an approach that often reflects the human interpretation of categories. Given two or more hierarchical sets of categories, our aim is to determine the correspondence between categories (e.g., approximate equivalence). Association rules are a useful tool in knowledge discovery from databases but are normally defined in terms of crisp rather than fuzzy categories. In this chapter, the authors describe a new method for calculating a fuzzy confidence value for association rules between fuzzy categories, using a novel approach based on mass assignment theory.

Parallel mining and application of fuzzy association rules

2006 ◽  
Vol 1 (2) ◽  
pp. 177-182
Author(s):  
Jian-jiang Lu ◽  
Bao-wen Xu ◽  
Xiao-feng Zou ◽  
Da-zhou Kang ◽  
Yan-hui Li ◽  
...  
Keyword(s):  
Association Rules ◽  
Fuzzy Association Rules ◽  
Parallel Mining

scholarly journals Mining Fuzzy Association Rules in the Framework of AFS Theory

2015 ◽  
Vol 2 (3) ◽  
pp. 261-270 ◽  
Author(s):  
Bo Wang ◽  
Xiao-dong Liu ◽  
Li-dong Wang
Keyword(s):  
Association Rules ◽  
Fuzzy Association Rules ◽  
Afs Theory

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.

On Fuzzy Modus Ponens to Assess Fuzzy Association Rules

2015 ◽  
pp. 269-276 ◽  
Author(s):  
Miguel Delgado ◽  
M. Dolores Ruiz ◽  
Daniel Sánchez ◽  
M. Amparo Vila
Keyword(s):  
Association Rules ◽  
Modus Ponens ◽  
Fuzzy Association Rules ◽  
Fuzzy Modus Ponens

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

2011 ◽  
Vol 145 ◽  
pp. 292-296
Author(s):  
Lee Wen Huang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Large Databases ◽  
Closed Itemsets ◽  
Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

Sign in / Sign up

Export Citation Format

Share Document