scholarly journals Research on Data Mining Framework Based on Improved Sequential Association Rule Discovery

2016 ◽  
Author(s):  
Qing Tan
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Rule Discovery ◽  
Association Rule Discovery

A New Classification Based on Association Algorithm

2010 ◽  
Vol 09 (01) ◽  
pp. 55-64 ◽  
Author(s):  
Fadi Thabtah ◽  
Qazafi Mahmood ◽  
Lee McCluskey ◽  
Hussein Abdel-Jaber
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Prediction Method ◽  
Computational Time ◽  
Rule Discovery ◽  
Associative Classification ◽  
Classification Problems ◽  
Association Rule Discovery ◽  
Similar Class ◽  
Class Labels

Associative classification is a branch in data mining that employs association rule discovery methods in classification problems. In this paper, we introduce a novel data mining method called Looking at the Class (LC), which can be utilised in associative classification approach. Unlike known algorithms in associative classification such as Classification based on Association rule (CBA), which combine disjoint itemsets regardless of their class labels in the training phase, our method joins only itemsets with similar class labels. This saves too many unnecessary itemsets combining during the learning step, and consequently results in massive saving in computational time and memory. Moreover, a new prediction method that utilises multiple rules to make the prediction decision is also developed in this paper. The experimental results on different UCI datasets reveal that LC algorithm outperformed CBA with respect to classification accuracy, memory usage, and execution time on most datasets we consider.

scholarly journals Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

2021 ◽  
Vol 1 (2) ◽  
pp. 54-66
Author(s):  
M. Hamdani Santoso
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
A Priori ◽  
Apriori Algorithm ◽  
Rule Discovery ◽  
Association Rule Discovery ◽  
Rule Method ◽  
Confidence Value ◽  
Method Show

Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.

scholarly journals Semantics-Based Classification of Rule Interestingness Measures

2009 ◽  
pp. 56-79 ◽  
Author(s):  
Julien Blanchard ◽  
Fabrice Guillet ◽  
Pascale Kuntz
Keyword(s):  
Association Rule ◽  
Rule Discovery ◽  
Interestingness Measures ◽  
Association Rule Discovery ◽  
The Subject ◽  
Rule Interestingness

Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the subject, the scope, and the nature of the measure. These criteria seem essential to grasp the meaning of the measures, and therefore to help the user to choose the ones (s)he wants to apply. Moreover, the classification allows one to compare the rules to closely related concepts such as similarities, implications, and equivalences. Finally, the classification shows that some interesting combinations of the criteria are not satisfied by any index.

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