Achieving k-Anonymity for Associative Classification in Incremental-Data Scenarios

2011 ◽  
pp. 54-63 ◽  
Author(s):  
Bowonsak Seisungsittisunti ◽  
Juggapong Natwichai
Keyword(s):  
Associative Classification

LAC: Library for associative classification

2020 ◽  
Vol 193 ◽  
pp. 105432 ◽  
Author(s):  
Francisco Padillo ◽  
Jose Maria Luna ◽  
Sebastian Ventura
Keyword(s):  
Associative Classification

An integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification

2016 ◽  
Vol 28 (12) ◽  
pp. 3967-3980 ◽  
Author(s):  
Nirase Fathima Abubacker ◽  
Azreen Azman ◽  
Shyamala Doraisamy ◽  
Masrah Azrifah Azmi Murad
Keyword(s):  
Fuzzy Approach ◽  
Associative Classification ◽  
Integrated Method ◽  
Neuro Fuzzy

ACRIPPER: A New Associative Classification Based on RIPPER Algorithm

2021 ◽  
Vol 20 (01) ◽  
pp. 2150013
Author(s):  
Mohammed Abu-Arqoub ◽  
Wael Hadi ◽  
Abdelraouf Ishtaiwi
Keyword(s):  
Decision Making ◽  
Class Imbalance ◽  
The Other ◽  
Associative Classification ◽  
Rule Based ◽  
New Approach ◽  
New Novel ◽  
Accuracy Measure ◽  
High Level ◽  
Substantial Interest

Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our paper, we address these challenges and propose a new novel AC approach based on the RIPPER algorithm, which we refer to as ACRIPPER. Our new approach combines the strength of the RIPPER algorithm with the classical AC method, in order to achieve: (1) a reduction in the number of rules being mined, especially those rules that are largely insignificant; (2) a high level of integration among the confidence and support of the rules on one hand and the class imbalance level in the prediction phase on the other hand. Our experimental results, using 20 different well-known datasets, reveal that the proposed ACRIPPER significantly outperforms the well-known rule-based algorithms RIPPER and J48. Moreover, ACRIPPER significantly outperforms the current AC-based algorithms CBA, CMAR, ECBA, FACA, and ACPRISM. Finally, ACRIPPER is found to achieve the best average and ranking on the accuracy measure.

ASSOCIATIVE CLASSIFICATION OF MAMMOGRAMS BASED ON PARALLEL MINING OF IMAGE BLOCKS

2012 ◽  
Vol 24 (06) ◽  
pp. 513-524
Author(s):  
Mohsen Alavash Shooshtari ◽  
Keivan Maghooli ◽  
Kambiz Badie
Keyword(s):  
Association Rules ◽  
Classification Systems ◽  
Classification Model ◽  
Automated Classification ◽  
Associative Classification ◽  
Classification Problems ◽  
Association Rules Mining ◽  
Parallel Mining ◽  
Transactional Databases ◽  
Unique Decision

One of the main objectives of data mining as a promising multidisciplinary field in computer science is to provide a classification model to be used for decision support purposes. In the medical imaging domain, mammograms classification is a difficult diagnostic task which calls for development of automated classification systems. Associative classification, as a special case of association rules mining, has been adopted in classification problems for years. In this paper, an associative classification framework based on parallel mining of image blocks is proposed to be used for mammograms discrimination. Indeed, association rules mining is applied to a commonly used mammography image database to classify digital mammograms into three categories, namely normal, benign and malign. In order to do so, first images are preprocessed and then features are extracted from non-overlapping image blocks and discretized for rule discovery. Association rules are then discovered through parallel mining of transactional databases which correspond to the image blocks, and finally are used within a unique decision-making scheme to predict the class of unknown samples. Finally, experiments are conducted to assess the effectiveness of the proposed framework. Results show that the proposed framework proved successful in terms of accuracy, precision, and recall, and suggest that the framework could be used as the core of any future associative classifier to support mammograms discrimination.

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