Discovering Both Positive and Negative Fuzzy Association Rules in Large Transaction Databases

2006 ◽  
Vol 10 (3) ◽  
pp. 287-294 ◽  
Author(s):  
Jianchao Han ◽  
◽  
Mohsen Beheshti
Keyword(s):  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Positive Association ◽  
Negative Association ◽  
Fuzzy Association Rules ◽  
Novel Approach ◽  
Negative Association Rules ◽  
Fuzzy Association Rule ◽  
Mining Algorithms

Mining association rules is an important task of dara mining and knowledge discovery. Traditional association rules mining is built on transaction databases, which has some limitations. Two of these limitations are 1) each transaction merely contains binary items, meaning that an item either occurs in a transaction or not; 2) only positive association rules are discovered, while negative associations are ignored. Mining fuzzy association rules has been proposed to address the first limitation, while mining algorithms for negative association rules have been developed to resolve the second limitation. In this paper, we combine these two approaches to propose a novel approach for mining both positive and negative fuzzy association rules. The interestingness measure for both positive and negative fuzzy association rule is proposed, the algorithm for mining these rules is described, and an illustrative example is presented to demonstrate how the measure and the algorithm work.

A Novel Approach on Negative Association Rules

2011 ◽  
pp. 1425-1430
Author(s):  
Ioannis N. Kouris
Keyword(s):  
Association Rules ◽  
Positive Association ◽  
Negative Influence ◽  
Negative Association ◽  
Association Rules Mining ◽  
Novel Approach ◽  
Negative Effect ◽  
Negative Association Rules ◽  
The Subject ◽  
Definition Of

Research in association rules mining has initially concentrated in solving the obvious problem of finding positive association rules; that is rules among items that exist in the stored transactions. It was only several years after that the possibility of finding also negative association rules became especially appealing and was investigated. Nevertheless researchers based their assumptions regarding negative association rules on the absence of items from transactions. This assumption though besides being dubious, since it equated the absence of an item with a conflict or negative effect on the rest items, it also brought out a series of computational problems with the amount of possible patterns that had to be examined and analyzed. In this work we give an overview of the works having engaged with the subject until now and present a novel view for the definition of negative influence among items.

scholarly journals Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Sajid Mahmood ◽  
Muhammad Shahbaz ◽  
Aziz Guergachi
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Negative Symptoms ◽  
Medical Science ◽  
Positive Association ◽  
Negative Association ◽  
Rule Mining ◽  
Huge Number ◽  
Significant Research ◽  
Negative Association Rules

Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets leading to the discovery of negative association rules (NARs). The discovery of infrequent itemsets is far more difficult than their counterparts, that is, frequent itemsets. These problems include infrequent itemsets discovery and generation of accurate NARs, and their huge number as compared with positive association rules. In medical science, for example, one is interested in factors which can either adjudicate the presence of a disease or write-off of its possibility. The vivid positive symptoms are often obvious; however, negative symptoms are subtler and more difficult to recognize and diagnose. In this paper, we propose an algorithm for discovering positive and negative association rules among frequent and infrequent itemsets. We identify associations among medications, symptoms, and laboratory results using state-of-the-art data mining technology.

A Survey on Fuzzy Association Rule Mining

2013 ◽  
Vol 9 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Harihar Kalia ◽  
Satchidananda Dehuri ◽  
Ashish Ghosh
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Quantitative Data ◽  
Association Rule Mining ◽  
Real Life ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Fuzzy Association Rule ◽  
Mining Algorithms ◽  
Fuzzy Association Rule Mining

Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.

Extracting of Positive and Negative Association Rules

2018 ◽  
Vol 6 (8) ◽  
pp. 421
Author(s):  
D. Gandhimathi ◽  
N. Anbazhagan
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Positive Association ◽  
Current Data ◽  
Negative Association ◽  
Rule Mining ◽  
Web Usage ◽  
Log Structure ◽  
Negative Association Rules

Association rules analysis is a basic technique to expose how items/patterns are associated to each other. There are two common ways to measure association such as Support and Confidence. Several methods have been proposed in the literature to diminish the number of extracted association rules. Association Rule Mining is one of the greatest current data mining techniques designed to group objects together from huge databases aiming to take out the motivating correlation and relation with massive quantity of data. Association rule mining is used to discover the associated patterns from datasets. In this paper, we propose association rules from new methods on web usage mining. Generally, web usage log structure has several records so we have to overcome those unwanted records from large dataset. First of all the pre-processed data from the NASA dataset is clustered by the popular K-Means algorithm. Subsequently, the matrix calculation is progressed on that data. Further, the associations are performed on filtered data and get rid of the final associated page results. Positive and negative association rules are gathered by using new algorithm with Annul Object (𝒜𝒪). Wherever the object “𝒜𝒪” is presented those rules are known as negative association rule.  Otherwise, the rules are positive association rules.

An Algorithm for Mining Multidimensional Positive and Negative Association Rules

2010 ◽  
Vol 171-172 ◽  
pp. 445-449 ◽  
Author(s):  
He Jiang ◽  
Ze Bai ◽  
Guo Ling Liu ◽  
Xiu Mei Luan
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Positive Association ◽  
Negative Association ◽  
Multidimensional Data ◽  
Rule Mining ◽  
Multidimensional Datasets ◽  
Single Positive ◽  
Negative Association Rules

Research on negative association rule in multidimensional data mining is few. In this paper, an algorithm MPNAR is put forward to mine positive and negative association rules in multidimensional data. With the help of the basis of the minimum support and minimum confidence, this algorithm divided the multidimensional datasets into infrequent itemsets and frequent itemsets. The negative association rules could be mined from infrequent itemsets. Relative to the single positive association rule mining, the new additional negative association rules need not repeatedly read database because two types of association rules were simultaneously mined. Experiments show that the algorithm method is effective and valuable.

Association Rules

2009 ◽  
pp. 1-10 ◽  
Author(s):  
Paul D. McNicholas ◽  
Yanchang Zhao
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Binary Data ◽  
Negative Association ◽  
Formal Definition ◽  
Rule Generation ◽  
Negative Association Rules ◽  
Definition Of ◽  
Pruning Methods

Association rules present one of the most versatile techniques for the analysis of binary data, with applications in areas as diverse as retail, bioinformatics, and sociology. In this chapter, the origin of association rules is discussed along with the functions by which association rules are traditionally characterised. Following the formal definition of an association rule, these functions – support, confidence and lift – are defined and various methods of rule generation are presented, spanning 15 years of development. There is some discussion about negations and negative association rules and an analogy between association rules and 2×2 tables is outlined. Pruning methods are discussed, followed by an overview of measures of interestingness. Finally, the post-mining stage of the association rule paradigm is put in the context of the preceding stages of the mining process.

Effective Mining of Weighted Fuzzy Association Rules

2010 ◽  
pp. 47-64 ◽  
Author(s):  
Maybin Muyeba ◽  
M. Sulaiman Khan ◽  
Frans Coenen
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Real Data ◽  
Closure Property ◽  
Data Sets ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Novel Approach ◽  
Downward Closure

A novel approach is presented for effectively mining weighted fuzzy association rules (ARs). The authors address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance wrt some user defined criteria. Most works on weighted association rule mining do not address the downward closure property while some make assumptions to validate the property. This chapter generalizes the weighted association rule mining problem with binary and fuzzy attributes with weighted settings. Their methodology follows an Apriori approach but employs T-tree data structure to improve efficiency of counting itemsets. The authors’ approach avoids pre and post processing as opposed to most weighted association rule mining algorithms, thus eliminating the extra steps during rules generation. The chapter presents experimental results on both synthetic and real-data sets and a discussion on evaluating the proposed approach.

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.

scholarly journals Generating a Condensed Representation for Positive and Negative Association Rules

2021 ◽  
pp. 175-186
Author(s):  
Bemarisika Parfait ◽  
André Totohasina
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Negative Association ◽  
Condensed Representation ◽  
Rule Set ◽  
Closed Itemsets ◽  
Negative Association Rules ◽  
Maximal Frequent Itemsets ◽  
Highly Correlated ◽  
Common Association

Given a large collection of transactions containing items, a basic common association rules problem is the huge size of the extracted rule set. Pruning uninteresting and redundant association rules is a promising approach to solve this problem. In this paper, we propose a Condensed Representation for Positive and Negative Association Rules representing non-redundant rules for both exact and approximate association rules based on the sets of frequent generator itemsets, frequent closed itemsets, maximal frequent itemsets, and minimal infrequent itemsets in database B. Experiments on dense (highly-correlated) databases show a significant reduction of the size of extracted association rule set in database B.

From Change Mining to Relevance Feedback

2009 ◽  
pp. 12-37 ◽  
Author(s):  
Mirko Boettcher ◽  
Georg Ruß ◽  
Detlef Nauck ◽  
Rudolf Kruse
Keyword(s):  
Association Rules ◽  
Relevance Feedback ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Novel Approach ◽  
Large Numbers ◽  
Textual Description ◽  
Rule Change ◽  
Mining Algorithms

Association rule mining typically produces large numbers of rules, thereby creating a second-order data mining problem: which of the generated rules are the most interesting? And: should interestingness be measured objectively or subjectively? To tackle the amount of rules that are created during the mining step, the authors propose the combination of two novel ideas: first, there is rule change mining, which is a novel extension to standard association rule mining which generates potentially interesting time-dependent features for an association rule. It does not require changes in the existing rule mining algorithms and can therefore be applied during post-mining of association rules. Second, the authors make use of the existing textual description of a rule and those newly derived objective features and combine them with a novel approach towards subjective interestingness by using relevance feedback methods from information retrieval. The combination of these two new approaches yields a powerful, intuitive way of exploring the typically vast set of association rules. It is able to combine objective and subjective measures of interestingness and will incorporate user feedback. Hence, it increases the probability of finding the most interesting rules given a large set of association rules.

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