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 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.

Classification and Association Rule Mining Technique for Predicting Chronic Kidney Disease

2020 ◽  
Vol 19 (01) ◽  
pp. 2040015
Author(s):  
Ahmad Alaiad ◽  
Hassan Najadat ◽  
Belal Mohsen ◽  
Khaled Balhaf
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Association Rule ◽  
Association Rule Mining ◽  
Support Vector ◽  
Classification Algorithms ◽  
Nearest Neighbour ◽  
Rule Mining ◽  
Medical Field ◽  
Efficient System

Background and objective: Chronic kidney disease (CKD) is one of the deadly diseases that can affect a lot of vital organs in the human body such as heart, liver, and lungs. Many individuals might be at early stage of kidney disease and not have any signs, which might lead to a sudden death. Previous research showed that early prediction of CKD is very important in the medical field for physicians’ decision-making and patients’ health and life. To this end, constructing an efficient prediction system for CKD, which is the goal of this paper, often reduces medical errors and overall healthcare cost. Methods: Classification and association rule mining techniques were integrated and utilised to construct an efficient system for predicting and diagnosing CKD and its causes using weka and SPSS as platform environments. In particular, five classification algorithms, namely, naive Bayes, decision tree, support vector machine, K-nearest neighbour, and JRip were used to achieve the research goal. In addition, Apriori algorithm was used to discover strong relationship rules between attributes. The experiments were conducted on real medical dataset collected from hospitals and patient monitoring systems. Results: The experiments achieved high accuracy of 98.50% for K-nearest neighbour (KNN) classifier and achieved 96.00% when using classier based on association rule (JRip). Conclusions: We conclude by showing that applying integrative approach by combining classification algorithms and association rule mining can significantly improve the classification accuracy and be more useful for CKD prediction. This research has also several theoretical and practical implications for the medical field and healthcare industry.

Analysis of Various Interestingness Measures in Class Association Rule Mining

2011 ◽  
Vol 4 (4) ◽  
pp. 295-304 ◽  
Author(s):  
Xianneng LI ◽  
Shingo MABU ◽  
Huiyu ZHOU ◽  
Kaoru SHIMADA ◽  
Kotaro HIRASAWA
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Interestingness Measures ◽  
Class Association Rule

Rare Class Association Rule Mining with Multiple Imbalanced Attributes

2010 ◽  
pp. 66-75 ◽  
Author(s):  
Huaifeng Zhang ◽  
Yanchang Zhao ◽  
Longbing Cao ◽  
Chengqi Zhang ◽  
Hans Bohlscheid
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Uniform Space ◽  
Rule Mining ◽  
Target Class ◽  
Left Hand ◽  
The Social ◽  
Rare Class ◽  
Class Association Rule ◽  
The Right

In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This algorithm is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through a standard algorithm while the rules with imbalanced attributes are mined based on newly defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied in the social security field. Although some attributes are severely imbalanced, rules with a minority of imbalanced attributes have been mined efficiently.

TEXT CLASSIFICATION USING MODIFIED MULTI CLASS ASSOCIATION RULE

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Siti Sakira Kamaruddin ◽  
Yuhanis Yusof ◽  
Husniza Husni ◽  
Mohammad Hayel Al Refai
Keyword(s):  
Text Classification ◽  
Association Rule ◽  
Classification Accuracy ◽  
Classification Problem ◽  
Frequent Pattern ◽  
Associative Classification ◽  
Vertical Data ◽  
Rule Method ◽  
Class Association Rule ◽  
Two Stages

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.

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