Evaluating pattern restrictions for associative classifiers

2020 ◽  
Vol 24 ◽  
pp. 105-122
Author(s):  
González-Méndez Andy ◽  
Martín Diana ◽  
Morales Eduardo ◽  
García-Borroto Milton
Keyword(s):  
Association Rule ◽  
Experimental Comparison ◽  
Rule Discovery ◽  
Classification Models ◽  
Associative Classification ◽  
Association Rule Discovery ◽  
Pattern Recognition Approach ◽  
Associative Classifiers ◽  
The Impact ◽  
First Time

Associative classification is a pattern recognition approach that integrates classification and association rule discovery to build accurate classification models. These models are formed by a collection of contrast patterns that fulfill some restrictions. In this paper, we introduce an experimental comparison of the impact of using different restrictions in the classification accuracy. To the best of our knowledge, this is the first time that such analysis is performed, deriving some interesting findings about how restrictions impact on the classification results. Contrasting these results with previously published papers, we found that their conclusions could be unintentionally biased by the restrictions they used. We found, for example, that the jumping restriction could severely damage the pattern quality in the presence of dataset noise. We also found that the minimal support restriction has a different effect in the accuracy of two associative classifiers, therefore deciding which one is the best depends on the support value. This paper opens some interesting lines of research, mainly in the creation of new restrictions and new pattern types by joining different restrictions.

scholarly journals A review of associative classification mining

2007 ◽  
Vol 22 (1) ◽  
pp. 37-65 ◽  
Author(s):  
FADI THABTAH
Keyword(s):  
Classification Systems ◽  
Quality Data ◽  
Data Sets ◽  
Rule Discovery ◽  
Associative Classification ◽  
Future Directions ◽  
Association Rule Discovery ◽  
Rule Pruning ◽  
Associative Classifiers ◽  
Pruning Rule

AbstractAssociative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper.

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.

Associative Classification Approaches: Review and Comparison

2014 ◽  
Vol 13 (03) ◽  
pp. 1450027 ◽  
Author(s):  
Neda Abdelhamid ◽  
Fadi Thabtah
Keyword(s):  
Association Rule ◽  
Rule Learning ◽  
Learning Rule ◽  
Data Representation ◽  
Associative Classification ◽  
Data Mining Approach ◽  
Association Rule Discovery ◽  
Multi Class Classification ◽  
Rule Pruning ◽  
New Research

Associative classification (AC) is a promising data mining approach that integrates classification and association rule discovery to build classification models (classifiers). In the last decade, several AC algorithms have been proposed such as Classification based Association (CBA), Classification based on Predicted Association Rule (CPAR), Multi-class Classification using Association Rule (MCAR), Live and Let Live (L3) and others. These algorithms use different procedures for rule learning, rule sorting, rule pruning, classifier building and class allocation for test cases. This paper sheds the light and critically compares common AC algorithms with reference to the abovementioned procedures. Moreover, data representation formats in AC mining are discussed along with potential new research directions.

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