scholarly journals A neural-networks associative classification method for association rule mining

2006 ◽  
Author(s):  
P. Sermswatsri ◽  
C. Srisa-an
Keyword(s):  
Neural Networks ◽  
Association Rule ◽  
Association Rule Mining ◽  
Classification Method ◽  
Associative Classification ◽  
Rule Mining

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

scholarly journals Retraction Note to: Mass classification method in mammograms using correlated association rule mining

2015 ◽  
Vol 27 (1) ◽  
pp. 251-251
Author(s):  
Aswini Kumar Mohanty ◽  
Manas Senapati ◽  
Swapnasikta Beberta ◽  
Saroj Kumar Lenka
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Classification Method ◽  
Rule Mining ◽  
Mass Classification

scholarly journals Prediction of By-Diseases in Diabetic Patients Using Associative Classification with Improved Classifier Accuracy for Decision Support System

2019 ◽  
Vol 8 (6) ◽  
pp. 2625-2628
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Diabetic Patients ◽  
Classification Algorithms ◽  
Data Sets ◽  
Classification Models ◽  
Associative Classification ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technique

Associative Classification in data mining technique formulates more and more simple methods and processes to find and predict the health problems like diabetes, tumors, heart problems, thyroid, cancer, malaria etc. The methods of classification combined with association rule mining gradually helps to predict large amount of data and also builds the accurate classification models for the future analysis. The data in medical area is sometimes vast and containss the information that relates to different diseases. It becomes difficult to estimate and analyze the disease problems that change from period to period based on severity. In this research paper, the use and need of associative classification for the medical data sets and the application of associative classification on the data in order to predict the by-diseases has been put front. The association rules in this context developed in training phase of data have predicted the chance of occurrence of other diseases in persons suffering with diabetes mellitus using Predictive Apriori. The associative classification algorithms like CAR is deployed in the context of accuracy measures.

RETRACTED ARTICLE: Mass classification method in mammograms using correlated association rule mining

2012 ◽  
Vol 23 (2) ◽  
pp. 273-281 ◽  
Author(s):  
Aswini Kumar Mohanty ◽  
Manas Senapati ◽  
Swapnasikta Beberta ◽  
Saroj Kumar Lenka
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Classification Method ◽  
Rule Mining ◽  
Retracted Article ◽  
Mass Classification

A novel linear assorted classification method based association rule mining with spatial data

Sadhana ◽  
2021 ◽  
Vol 46 (1) ◽  
Author(s):  
P D Sheena Smart ◽  
K K Thanammal ◽  
S S Sujatha
Keyword(s):  
Spatial Data ◽  
Association Rule ◽  
Association Rule Mining ◽  
Classification Method ◽  
Rule Mining

A New Approach to Associative Classification Based on Binary Multi-Objective Particle Swarm Optimization

2012 ◽  
pp. 230-252
Author(s):  
Madhabananda Das ◽  
Rahul Roy ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Extraction ◽  
Classification Rules ◽  
Associative Classification ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Rule Selection

Associative classification rule mining (ACRM) methods operate by association rule mining (ARM) to obtain classification rules from a previously classified data. In ACRM, classifiers are designed through two phases: rule extraction and rule selection. In this paper, the ACRM problem is treated as a multi-objective problem rather than a single objective one. As the problem is a discrete combinatorial optimization problem, it was necessary to develop a binary multi-objective particle swarm optimization (BMOPSO) to optimize the measure like coverage and confidence of association rule mining (ARM) to extract classification rules in rule extraction phase. In rule selection phase, a small number of rules are targeted from the extracted rules by BMOPSO to design an accurate and compact classifier which can maximize the accuracy of the rule sets and minimize their complexity simultaneously. Experiments are conducted on some of the University of California, Irvine (UCI) repository datasets. The comparative result of the proposed method with other standard classifiers confirms that the new proposed approach can be a suitable method for classification.

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