Positive and negative association rule mining on XML data streams in database as a service concept

2012 ◽  
Vol 39 (8) ◽  
pp. 7503-7511 ◽  
Author(s):  
Samet Çokpınar ◽  
Taflan İmre Gündem
Keyword(s):  
Data Streams ◽  
Association Rule ◽  
Association Rule Mining ◽  
Negative Association ◽  
Rule Mining ◽  
Xml Data ◽  
Service Concept ◽  
Database As A Service

scholarly journals On Efficient and Effective Association Rule Mining from XML Data

2004 ◽  
pp. 497-507 ◽  
Author(s):  
Ji Zhang ◽  
Tok Wang Ling ◽  
Robert M. Bruckner ◽  
A Min Tjoa ◽  
Han Liu
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Xml Data

Frequent Pattern Discovery and Association Rule Mining of XML Data

Data Mining ◽  
2013 ◽  
pp. 859-879
Author(s):  
Qin Ding ◽  
Gnanasekaran Sundarraj
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Discovery ◽  
Frequent Pattern ◽  
Future Research ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Xml Data ◽  
Art Research

Finding frequent patterns and association rules in large data has become a very important task in data mining. Various algorithms have been proposed to solve such problems, but most algorithms are only applicable to relational data. With the increasing use and popularity of XML representation, it is of importance yet challenging to find solutions to frequent pattern discovery and association rule mining of XML data. The challenge comes from the complexity of the structure in XML data. In this chapter, we provide an overview of the state-of-the-art research in content-based and structure-based mining of frequent patterns and association rules from XML data. We also discuss the challenges and issues, and provide our insight for solutions and future research directions.

A Framework for Efficient Association Rule Mining in XML Data

2006 ◽  
Vol 17 (3) ◽  
pp. 19-40 ◽  
Author(s):  
Ji Zhang ◽  
Han Liu ◽  
Tok Wang Ling ◽  
Robert M. Bruckner ◽  
A Min Tjoa
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Xml Data

Frequent Pattern Discovery and Association Rule Mining of XML Data

2011 ◽  
pp. 243-263 ◽  
Author(s):  
Qin Ding ◽  
Gnanasekaran Sundarraj
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Discovery ◽  
Frequent Pattern ◽  
Future Research ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Xml Data ◽  
Art Research

Finding frequent patterns and association rules in large data has become a very important task in data mining. Various algorithms have been proposed to solve such problems, but most algorithms are only applicable to relational data. With the increasing use and popularity of XML representation, it is of importance yet challenging to find solutions to frequent pattern discovery and association rule mining of XML data. The challenge comes from the complexity of the structure in XML data. In this chapter, we provide an overview of the state-of-the-art research in content-based and structure-based mining of frequent patterns and association rules from XML data. We also discuss the challenges and issues, and provide our insight for solutions and future research directions.

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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