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.

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.

ACNB

2013 ◽  
Vol 8 (1) ◽  
pp. 23-35 ◽  
Author(s):  
Fadi Odeh ◽  
Nijad Al-Najdawi
Keyword(s):  
Association Rule ◽  
Text Categorization ◽  
Prediction Method ◽  
Associative Classification ◽  
Bayesian Algorithm ◽  
New Approach ◽  
Naive Bayesian ◽  
Association Rule Discovery ◽  
Naïve Bayesian ◽  
Traditional Classification

Integrating association rule discovery and classification in data mining brings a new approach known as associative classification. Associative classification is a promising approach that often constructs more accurate classification models (classifiers) than the traditional classification approaches such as decision trees and rule induction. In this research, the authors investigate the use of associative classification on the high dimensional data in text categorization. This research focuses on prediction, a very important step in classification, and introduces a new prediction method called Associative Classification Mining based on Naïve Bayesian method. The running time is decreased by removing the ranking procedure that is usually the first step in ranking the derived Classification Association Rules. The prediction method is enhanced using the Naïve Bayesian Algorithm. The results of the experiments demonstrate high classification accuracy.

PREDICTION PHASE IN ASSOCIATIVE CLASSIFICATION MINING

2011 ◽  
Vol 21 (06) ◽  
pp. 855-876 ◽  
Author(s):  
FADI THABTAH ◽  
WAEL HADI ◽  
NEDA ABDELHAMID ◽  
AYMAN ISSA
Keyword(s):  
Prediction Methods ◽  
Associative Classification ◽  
Rule Mining ◽  
Data Mining Approach ◽  
Average Confidence ◽  
Classification Prediction ◽  
Rule Pruning ◽  
Break Even Point ◽  
Data Collections ◽  
Review Current

Associative classification (AC) is an important data mining approach which effectively integrates association rule mining and classification. Prediction of test data is a fundamental step in classification that impacts the outputted system accuracy. In this paper, we present three new prediction methods (Dominant Class Label, Highest Average Confidence per Class, Full Match Rule) and one rule pruning procedure (Partial Matching) in AC. Furthermore, we review current prediction methods in AC. Experimental results on large English and Arabic text categorisation data collections (Reuters, SPA) using the proposed prediction methods and other popular classification algorithms (SVM, KNN, NB, BCAR, MCAR, C4.5, etc.), have been conducted. The bases of the comparison in the experiments are classification accuracy and the Break-Even-Point (BEP) evaluation measures. The results reveal that our prediction methods are very competitive with reference to BEP if compared with known AC prediction approaches such as those of 2-PS, ARC-BC and BCAR. Moreover, the proposed prediction methods outperform other existing methods in traditional classification approaches such as decision trees, and probabilistic with regards to accuracy. Finally, the results indicate that using the proposed pruning procedure in AC improved the accuracy of the outputted classifier.

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.

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.

MAC: A Multiclass Associative Classification Algorithm

2012 ◽  
Vol 11 (02) ◽  
pp. 1250011 ◽  
Author(s):  
Neda Abdelhamid ◽  
Aladdin Ayesh ◽  
Fadi Thabtah ◽  
Samad Ahmadi ◽  
Wael Hadi
Keyword(s):  
Data Mining ◽  
Rule Induction ◽  
Classification Systems ◽  
Data Repository ◽  
Traditional Learning ◽  
Data Sets ◽  
Learning Approaches ◽  
Associative Classification ◽  
Data Mining Approach ◽  
Association Rule Discovery

Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers). Several research studies reveal that AC normally generates higher accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and decision trees. This paper proposes a new multiclass AC algorithm called MAC. The proposed algorithm employs a novel method for building the classifier that normally reduces the resulting classifier size in order to enable end-user to more understand and maintain it. Experimentations against 19 different data sets from the UCI data repository and using different common AC and traditional learning approaches have been conducted with reference to classification accuracy and the number of rules derived. The results show that the proposed algorithm is able to derive higher predictive classifiers than rule induction (RIPPER) and decision tree (C4.5) algorithms and very competitive to a known AC algorithm named MCAR. Furthermore, MAC is also able to produce less number of rules than MCAR in normal circumstances (standard support and confidence thresholds) and in sever circumstances (low support and confidence thresholds) and for most of the data sets considered in the experiments.

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