An efficient method for pruning redundant negative and positive association rules

2020 ◽  
Vol 393 ◽  
pp. 245-258 ◽  
Author(s):  
Xiangjun Dong ◽  
Feng Hao ◽  
Long Zhao ◽  
Tiantian Xu
Keyword(s):  
Association Rules ◽  
Efficient Method ◽  
Positive Association

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.

A Study of Association Rules Mining Algorithms Based on Adaptive Support

2010 ◽  
Vol 108-111 ◽  
pp. 436-440
Author(s):  
Yue Shun He ◽  
Ping Du
Keyword(s):  
Association Rules ◽  
Efficient Method ◽  
User Needs ◽  
Association Rules Mining ◽  
Minimum Support ◽  
The Core ◽  
Minimum Number ◽  
Adaptive Support ◽  
Mining Algorithms ◽  
Specific Number

This paper presents an adaptive support for Boolean algorithm for mining association rules, the Algorithm does not require minimum support from outside, in the mining process of the algorithm will be based on user needs the minimum number of rules automatically adjust the scope of support to produce the specific number of rules, the algorithm number of rules for the user needs to generate the rules to a certain extent, reduce excavation time, avoid the artificial blindness specified minimum support. In addition, the core of the algorithm is using an efficient method of Boolean-type mining, using the logical OR, AND, and XOR operations to generate association rules, to avoided the candidate itemsets generated In the mining process, and only need to scan the database once, so the algorithm has a certain efficiency.

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.

scholarly journals Efficient Method for Mining Multiple-Level and Generalized Association Rules

2011 ◽  
Vol 22 (12) ◽  
pp. 2965-2980 ◽  
Author(s):  
Yu-Xing MAO ◽  
Tong-Bing CHEN ◽  
Bai-Le SHI
Keyword(s):  
Association Rules ◽  
Efficient Method ◽  
Multiple Level ◽  
Generalized Association Rules

A Frequent Item Sets Computing Algorithm Based on MapReduce

2013 ◽  
Vol 380-384 ◽  
pp. 1701-1704
Author(s):  
Da Wei Jin ◽  
Jie Song ◽  
Bin Liu ◽  
Cheng Zhao
Keyword(s):  
Association Rules ◽  
Efficient Method ◽  
Traditional Method ◽  
Good Stability ◽  
Data Sets ◽  
Single Node ◽  
Computing Algorithm ◽  
Frequent Item ◽  
Frequent Item Sets

Traditional method mining for association rules between items in large and grand data sets is inefficient. In this paper we present an efficient method called BPMRA which is based on mapreduce and partition. We have compared BPMRA algorithm based multi-node and partition based single node method and performed some experiments. It turns out that BPMRA possesses high parallelism good stability and scalability, especially suitable for mining for association rules in large and grand data sets.

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.

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