Associative Classification in Multi-label Classification: an Investigative Study

Raed Alazaidah ◽  
Mohammed Almaiah ◽  
Moath luwaici
2007 ◽  
Vol 22 (1) ◽  
pp. 37-65 ◽  

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.

2014 ◽  
Vol 30 (4) ◽  
pp. 752-770 ◽  
Nattapon Harnsamut ◽  
Juggapong Natwichai ◽  
Xingzhi Sun ◽  
Xue Li

2016 ◽  
Vol 78 (8-2) ◽  
Siti Sakira Kamaruddin ◽  
Yuhanis Yusof ◽  
Husniza Husni ◽  
Mohammad Hayel Al Refai

This paper presents text classification using a modified Multi Class Association Rule Method. The method is based on Associative Classification which combines classification with association rule discovery. Although previous work proved that Associative Classification produces better classification accuracy compared to typical classifiers, the study on applying Associative Classification to solve text classification problem are limited due to the common problem of high dimensionality of text data and this will consequently results in exponential number of generated classification rules. To overcome this problem the modified Multi-Class Association Rule Method was enhanced in two stages. In stage one the frequent pattern are represented using a proposed vertical data format to reduce the text dimensionality problem and in stage two the generated rule was pruned using a proposed Partial Rule Match to reduce the number of generated rules. The proposed method was tested on a text classification problem and the result shows that it performed better than the existing method in terms of classification accuracy and number of generated rules.

2014 ◽  
Vol 13 (03) ◽  
pp. 1450027 ◽  
Neda Abdelhamid ◽  
Fadi Thabtah

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.

2015 ◽  
Vol 2 (39) ◽  
pp. 212
Olga Nikolaevna Tushkanova ◽  
Vladimir Ivanovich Gorodetski

2017 ◽  
Vol 4 (1) ◽  
Luca Venturini ◽  
Elena Baralis ◽  
Paolo Garza

Sign in / Sign up

Export Citation Format

Share Document