Associative classification with a new condenseness measure

Associative classification, which uses association rules for classification, has achieved high accuracy in comparison with other classification approaches. However, the confidence measure, which is used to select association rules for classification, may not conform to the prediction accuracy of the rules. In this paper, we propose a measure for association rules called prediction confidence to measure the accuracy of the prediction of association rules. An approach for estimating the prediction confidence of a rule is also given. The use of prediction confidence instead of confidence measure helps gather better association rules for associative classification. As a result, a more accurate associative classifier can be constructed using the prediction confidence measure.

Associative classification is a supervised classification method. Many experimental studies have shown that associative classification is a promising approach. However, the latter suffer from a major drawback: the huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we propose in this paper a new direct associative classification method called IGARC, an improvement of GARC approach that extracts directly generic associative classification rules from a training set in order to reduce the number of associative classification rules without jeopardising the classification accuracy. Moreover, we propose an algorithm called PN-GARC that deals with negative classification rules. Considering negated items in classification framework provides additional information describing the data and reduces the conflicts while classifying new objects. Nevertheless, there are a sheer number of rules when considering negated items. That is why, we will explore generic classification rules both negative and positive ones in order to study their behaviour and their usefulness on the studied datasets. A detailed description of IGARC method is presented, as well as the experimentation study on 12 benchmark datasets proving that it is highly competitive in terms of accuracy in comparison with popular classification approaches.

Discovery of class association rules from the enormous amount of database is considered as a vital task in data mining. Nowadays, the development of computer technologies, for example, database management system and data storage has given the stage for gathering and dealing with a lot of information. Finding frequent class association rules combines two significant data mining techniques such as association rule mining and classification for maximum accuracy. Since this hybrid mining yields a large number of class rules during database scan, the processing time will increases significantly. Moreover, many of these rules may be irrelevant and sometimes it misses the most important rules during classifier construction. Rare lazy learning associative classification is the process of recognizing rare class rules without generating classifier construction by focusing on the useful features from patient data of the test instance which is important for rule generation. This method is profoundly appropriate for biomedical field like heart disease prediction system which requires higher accuracy. The proposed algorithm uses simple local caching mechanism on heart disease data and constructing classifier using cogency measure that will make the lazy associative classification fast and achieve higher accuracy than traditional algorithms. Experimental results show that the proposed cogency-based algorithm is more efficient than traditional algorithms for heart disease with high prediction accuracy and the proposed algorithm may also be useful in anomaly detection, fraud detection, detection of network failures and many more bio medical areas.

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.

A compact and understandable associative classifier based on overall coverage

Exploring ant-based algorithms for gene expression data analysis

The relations of the logical calculi of association rules and of the measures of the interestingness of association rules are studied. The logical calculi of association rules, 4ft-quantifiers, and known classes of association rules are briefly introduced. New 4ft-quantifiers and association rules are defined by the application of suitable thresholds to known measures of interestingness. It is proved that some of the new 4ft-quantifiers are related to known classes of association rules with important properties. It is shown that new interesting classes of association rules can be defined on the basis of other new 4ft-quantifiers and several results concerning new classes are proved. Open problems are introduced.KeywordsAssociation RuleData MatrixPredicate CalculusInterestingness MeasureLogical AspectThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In business association rules being considered as important assets, play a vital role in its productivity and growth. Different business partnership share association rules in order to explore the capabilities to make effective decision for enhance-ment of business and core capabilities. The fuzzy association rule mining approach emerged out of the necessity to mine quantitative data regularly present in database. An association rule is sensitive when it violates few rules and regulation for sharing particular nature of information to third world. Like classical association rules, there is a need for some privacy measures to be taken for retaining the standards and importance of fuzzy association rules. Privacy preservation is used for valu-able information extraction and minimizing the risk of sensitive information disclosure. Our proposed model mainly focuses to secure the sensitive information revealing association rules. In our model, sensitive fuzzy association rules are secured by identifying sensitive fuzzy item to perturb fuzzified dataset. The resulting transformed FARs are analyzed to conclude/calculate the accuracy level of our model in context of newly generated fuzzy association rules, hidden rules and lost rules. Extensive experiments are carried out in order to demonstrate the results of our proposed model. Privacy preservation of maximum number of sensitive FARs by keeping minimum perturbation highlights the significance of our model.

In recent years, the use of data mining techniques has gained significance in healthcare applications. The appropriate data mining techniques extract interesting affinities/associations between patient’s signs and symptoms, thus providing reasonable decision-making for the diagnosis and prognosis of the disease. Associative Classification (AC) is a contemporary technique, which uses the Class Association Rules (CARs) to build the classification system. Classification based on Predictive Association Rule (CPAR) is one of the popular AC algorithms, which utilise FOIL's Information Gain measure to select the best attributes for the generation of CARs. This work attempts to explore a suitable attribute selection measure and an error estimate measure that can best fit into the existing CPAR algorithm to construct an efficient rule-based classifier. Thus the performance of CPAR has been analyzed by applying alternative attribute selection measures such as Gain Ratio (GR) and Mutual Information Gain (IG) instead of FOIL’s Information Gain. Moreover, two error estimate measures such as Laplace accuracy (“La”) and Likelihood ratio statistic (“Lr”) are used for rule evaluation and best k-rule selection tasks of CPAR. This work analyzes the performance of CPAR-GR and CPAR-IG with the existing CPAR algorithm in terms of classifier accuracy. From the results, it was found that the use of “GR” and “IG” within CPAR yields accuracy higher than the existing CPAR. Significant differences in the performances of CPAR, CPAR-GR, and CPAR-IG are identified and demonstrated by experiments, and statistical tests using healthcare datasets taken from the UCI machine learning repository.

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.

A novel software defect prediction approach via weighted classification based on association rule mining

Associative classification based on the Transferable Belief Model

The initial decision after treatment in the hospital emergency room is very important because apart from being an indicator of the quality of care for emergency room practitioners, it is also needed to achieve health goals, namely improving the quality of critical care and preventing death. The initial decision was also for an unplanned ICU transfer. Unplanned ICU transfer is the transfer of patients who originally came from the ER (Emergency Room), then to the Inpatient Room (having been treated for 24-48 hours), then to the ICU. Many studies have been carried out to predict the initial decision of unplanned ICU transfer using univariate analysis, logistic regression analysis, and association rules. The association rule algorithm generates rules between patient diagnosis features that form a decision model for unplanned ICU transfers, so it is essential to get an association rule algorithm that is more efficient in generating rules. In this study, we compare two association rule algorithms to get a more efficient algorithm; then, the rules are used to form a decision model for unplanned ICU transfers. The study results obtained that the Apriori algorithm requires a completion time of 3 ms and the FP-Growth algorithm requires a completion time of 31 ms. Hence, the FP-Growth algorithm is 28 ms more efficient than the Apriori algorithm, while the resulting rule generation is the same number of 67 rules. Only 11 rules meet the minsupp and minconf threshold and include the set of Class Association Rules (CAR), which are used to form a decision model for unplanned ICU transfers with binary integer programming

Stock market prediction with high accuracy has always been an interesting subject for most investors and professional analysts. Data mining techniques are providing great aid to extract interesting and hidden knowledge from datasets. Financial data mining tools assist investors in their investment decisions, thereby reducing their investment risks. Associative classification rule mining is a promising approach in data mining that utilises the association rule discovery techniques to construct classification systems, also known as associative classifiers. This paper aims to develop an intelligent transaction system based on associative classification rule mining (ACR) and phenotypic artificial immune system (AIS) which discovers trading rules from numerical indicators. A new fitness function as a different measure of quality for quantitative association is suggested considering interestingness of rules. Based on the empirical studies on the top eight companies in the S&P 500 stocks, observed results demonstrate the superior prediction accuracy over the genetic algorithm based technique and the 'buy and hold' strategy.

Relations of logical calculi of association rules to measures of interestingness of association rules are studied. Logical calculi of association rules, 4ft-quantifiers and important classes of association rules are briefly introduced. New 4ft-quantifiers and association rules are defined by applications of suitable thresholds to several known measures of interestingness. It is proved that some of new 4ft-quantifiers constitute rules that belong to known classes of rules. It is shown that new interesting classes of rules can be defined on the basis of additional new 4ft-quantifiers. Some additional results concerning new classes of rules are proved. Open problems are introduced.