Journal articles: 'Associative classification rule base'

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Zhou, Zhongmei. "A New Classification Approach Based on Multiple Classification Rules." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/818253.

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A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when the minimum support is set to be low. It is difficult to select a high quality rule set for classification. Second, the accuracy of associative classification depends on the setting of the minimum support and the minimum confidence. In comparison with associative classification, some improved traditional rule-based classification approaches often produce a classification rule set that plays an important role in prediction. Thus, some improved traditional rule-based classification approaches not only achieve better efficiency than associative classification but also get higher accuracy. In this paper, we put forward a new classification approach called CMR (classification based on multiple classification rules). CMR combines the advantages of both associative classification and rule-based classification. Our experimental results show that CMR gets higher accuracy than some traditional rule-based classification methods.

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Thabtah, Fadi, Suhel Hammoud, and Hussein Abdel-Jaber. "Parallel Associative Classification Data Mining Frameworks Based MapReduce." Parallel Processing Letters 25, no. 02 (June 2015): 1550002. http://dx.doi.org/10.1142/s0129626415500024.

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Associative classification (AC) is a research topic that integrates association rules with classification in data mining to build classifiers. After dissemination of the Classification-based Association Rule algorithm (CBA), the majority of its successors have been developed to improve either CBA's prediction accuracy or the search for frequent ruleitems in the rule discovery step. Both of these steps require high demands in processing time and memory especially in cases of large training data sets or a low minimum support threshold value. In this paper, we overcome the problem of mining large training data sets by proposing a new learning method that repeatedly transforms data between line and item spaces to quickly discover frequent ruleitems, generate rules, subsequently rank and prune rules. This new learning method has been implemented in a parallel Map-Reduce (MR) algorithm called MRMCAR which can be considered the first parallel AC algorithm in the literature. The new learning method can be utilised in the different steps within any AC or association rule mining algorithms which scales well if contrasted with current horizontal or vertical methods. Two versions of the learning method (Weka, Hadoop) have been implemented and a number of experiments against different data sets have been conducted. The ground bases of the comparisons are classification accuracy and time required by the algorithm for data initialization, frequent ruleitems discovery, rule generation and rule pruning. The results reveal that MRMCAR is superior to both current AC mining algorithms and rule based classification algorithms in improving the classification performance with respect to accuracy.

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Thabtah, Fadi, Qazafi Mahmood, Lee McCluskey, and Hussein Abdel-Jaber. "A New Classification Based on Association Algorithm." Journal of Information & Knowledge Management 09, no. 01 (March 2010): 55–64. http://dx.doi.org/10.1142/s0219649210002486.

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Associative classification is a branch in data mining that employs association rule discovery methods in classification problems. In this paper, we introduce a novel data mining method called Looking at the Class (LC), which can be utilised in associative classification approach. Unlike known algorithms in associative classification such as Classification based on Association rule (CBA), which combine disjoint itemsets regardless of their class labels in the training phase, our method joins only itemsets with similar class labels. This saves too many unnecessary itemsets combining during the learning step, and consequently results in massive saving in computational time and memory. Moreover, a new prediction method that utilises multiple rules to make the prediction decision is also developed in this paper. The experimental results on different UCI datasets reveal that LC algorithm outperformed CBA with respect to classification accuracy, memory usage, and execution time on most datasets we consider.

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Das, Madhabananda, Rahul Roy, Satchidananda Dehuri, and Sung-Bae Cho. "A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization." International Journal of Applied Metaheuristic Computing 2, no. 2 (April 2011): 51–73. http://dx.doi.org/10.4018/jamc.2011040103.

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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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Abdelhamid, Neda, Aladdin Ayesh, and Wael Hadi. "Multi-Label Rules Algorithm Based Associative Classification." Parallel Processing Letters 24, no. 01 (March 2014): 1450001. http://dx.doi.org/10.1142/s0129626414500017.

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Current associative classification (AC) algorithms generate only the most obvious class linked with a rule in the training data set and ignore all other classes. We handle this problem by proposing a learning algorithm based on AC called Multi-label Classifiers based Associative Classification (MCAC) that learns rules associated with multiple classes from single label data. MCAC algorithm extracts classifiers from the whole training data set discovering all possible classes connected with a rule as long as they have sufficient training data representation. Another distinguishing feature of the MCAC algorithm is the classifier building method that cuts down the number of rules treating one known problem in AC mining which is the exponential growth of rules. Experimentations using real application data related to a complex scheduling problem known as the trainer timetabling problem reveal that MCAC's predictive accuracy is highly competitive if contrasted with known AC algorithms.

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Hasanpour, Hesam, Ramak Ghavamizadeh Meibodi, and Keivan Navi. "Improving rule-based classification using Harmony Search." PeerJ Computer Science 5 (November 18, 2019): e188. http://dx.doi.org/10.7717/peerj-cs.188.

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Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

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He, Cong, and Han Tong Loh. "Pattern-oriented associative rule-based patent classification." Expert Systems with Applications 37, no. 3 (March 15, 2010): 2395–404. http://dx.doi.org/10.1016/j.eswa.2009.07.069.

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Zhang, Shou Juan, and Quan Zhou. "A Novel Efficient Classification Algorithm Based on Class Association Rules." Applied Mechanics and Materials 135-136 (October 2011): 106–10. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.106.

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A novel classification algorithm based on class association rules is proposed in this paper. Firstly, the algorithm mines frequent items and rules only in one phase. Then, the algorithm ranks rules that pass the support and confidence thresholds using a global sorting method according to a series of parameters, including confidence, support, antecedent cardinality, class distribution frequency, item row order and rule antecedent length. Classifier building is based on rule items that do not overlap in the training phase and rule items that each training instance is covered by only a single rule. Experimental results on the 8 datasets from UCI ML Repository show that the proposed algorithm is highly competitive when compared with the C4.5,CBA,CMAR and CPAR algorithms in terms of classification accuracy and efficiency. This algorithm can offer an available associative classification technique for data mining.

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Mattiev, Jamolbek, and Branko Kavsek. "Coverage-Based Classification Using Association Rule Mining." Applied Sciences 10, no. 20 (October 9, 2020): 7013. http://dx.doi.org/10.3390/app10207013.

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Building accurate and compact classifiers in real-world applications is one of the crucial tasks in data mining nowadays. In this paper, we propose a new method that can reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier that selects “strong” class association rules based on overall coverage of the learning set. The advantage of the proposed classifier is that it generates significantly smaller rules on bigger datasets compared to traditional classifiers while maintaining the classification accuracy. We also discuss how the overall coverage of such classifiers affects their classification accuracy. Performed experiments measuring classification accuracy, number of classification rules and other relevance measures such as precision, recall and f-measure on 12 real-life datasets from the UCI ML repository (Dua, D.; Graff, C. UCI Machine Learning Repository. Irvine, CA: University of California, 2019) show that our method was comparable to 8 other well-known rule-based classification algorithms. It achieved the second-highest average accuracy (84.9%) and the best result in terms of average number of rules among all classification methods. Although not achieving the best results in terms of classification accuracy, our method proved to be producing compact and understandable classifiers by exhaustively searching the entire example space.

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Hadi, Wa'el, Qasem A. Al-Radaideh, and Samer Alhawari. "Integrating associative rule-based classification with Naïve Bayes for text classification." Applied Soft Computing 69 (August 2018): 344–56. http://dx.doi.org/10.1016/j.asoc.2018.04.056.

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Rajab, Khairan D. "New Associative Classification Method Based on Rule Pruning for Classification of Datasets." IEEE Access 7 (2019): 157783–95. http://dx.doi.org/10.1109/access.2019.2950374.

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Shooshtari, Mohsen Alavash, Keivan Maghooli, and Kambiz Badie. "ASSOCIATIVE CLASSIFICATION OF MAMMOGRAMS BASED ON PARALLEL MINING OF IMAGE BLOCKS." Biomedical Engineering: Applications, Basis and Communications 24, no. 06 (December 2012): 513–24. http://dx.doi.org/10.4015/s1016237212500470.

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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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Mattiev, Jamolbek, and Branko Kavsek. "Distance based clustering of class association rules to build a compact, accurate and descriptive classifier." Computer Science and Information Systems, no. 00 (2020): 37. http://dx.doi.org/10.2298/csis200430037m.

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Huge amounts of data are being collected and analyzed nowadays. By using the popular rule-learning algorithms, the number of rule discovered on those ?big? datasets can easily exceed thousands. To produce compact, understandable and accurate classifiers, such rules have to be grouped and pruned, so that only a reasonable number of them are presented to the end user for inspection and further analysis. In this paper, we propose new methods that are able to reduce the number of class association rules produced by ?classical? class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose new associative classifiers, called DC, DDC and CDC, that use distance-based agglomerative hierarchical clustering as a post-processing step to reduce the number of its rules, and in the rule-selection step, we use different strategies (based on database coverage and cluster center) for each algorithm. Experimental results performed on selected datasets from the UCI ML repository show that our classifiers are able to learn classifiers containing significantly fewer rules than state-of-the-art rule learning algorithms on datasets with a larger number of examples. On the other hand, the classification accuracy of the proposed classifiers is not significantly different from state-of-the-art rule-learners on most of the datasets.

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Abu-Arqoub, Mohammed, Wael Hadi, and Abdelraouf Ishtaiwi. "ACRIPPER: A New Associative Classification Based on RIPPER Algorithm." Journal of Information & Knowledge Management 20, no. 01 (March 2021): 2150013. http://dx.doi.org/10.1142/s0219649221500131.

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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.

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Craiger, J. Philip, Michael D. Coovert, and Mark S. Teachout. "Predicting Job Performance with a Fuzzy Rule-Based System." International Journal of Information Technology & Decision Making 02, no. 03 (September 2003): 425–44. http://dx.doi.org/10.1142/s0219622003000744.

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Classification problems affect all organizations. Important decisions affecting an organization's effectiveness include predicting the success of job applicants and the matching and assignment of individuals from a pool of applicants to available positions. In these situations, linear mathematical models are employed to optimize the allocation of an organization's human resources.Use of linear techniques may be problematic, however, when relationships between predictor and criterion are nonlinear. As an alternative, we developed a fuzzy associative memory (FAM: a rule-based system based on fuzzy sets and logic) and used it to derive predictive (classification) equations composed of measures of job experience and job performance. The data consisted of two job experience factors used to predict measures of job performance for four US Air Force job families. The results indicated a nonlinear relationship between experience and performance for three of the four data sets. The overall classification accuracy was similar for the two systems, although the FAM provided better classification for two of the jobs. We discuss the apparent nonlinear relationships between experience and performance, and the advantages and implications of using these systems to develop and describe behavioral models.

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Rak, Rafal, Lukasz Kurgan, and Marek Reformat. "A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation." Data & Knowledge Engineering 64, no. 1 (January 2008): 171–97. http://dx.doi.org/10.1016/j.datak.2007.05.006.

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Agrawal, Shivangee, and Nivedita Bairagi. "A Survey for Association Rule Mining in Data Mining." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 245. http://dx.doi.org/10.23956/ijarcsse.v7i8.58.

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Data mining, also identified as knowledge discovery in databases has well-known its place as an important and significant research area. The objective of data mining (DM) is to take out higher-level unknown detail from a great quantity of raw data. DM has been used in a variety of data domains. DM can be considered as an algorithmic method that takes data as input and yields patterns, such as classification rules, itemsets, association rules, or summaries, as output. The ’classical’ associations rule issue manages the age of association rules by support portraying a base level of confidence and support that the roduced rules should meet. The most standard and classical algorithm used for ARM is Apriori algorithm. It is used for delivering frequent itemsets for the database. The essential thought behind this algorithm is that numerous passes are made the database. The total usage of association rule strategies strengthens the knowledge management process and enables showcasing faculty to know their customers well to give better quality organizations. In this paper, the detailed description has been performed on the Genetic algorithm and FP-Growth with the applications of the Association Rule Mining.

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Baadel, Said, and Joan Lu. "Data Analytics: Intelligent Anti-Phishing Techniques Based on Machine Learning." Journal of Information & Knowledge Management 18, no. 01 (March 2019): 1950005. http://dx.doi.org/10.1142/s0219649219500059.

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According to the international body Anti-Phishing Work Group (APWG), phishing activities have skyrocketed in the last few years and more online users are becoming susceptible to phishing attacks and scams. While many online users are vulnerable and naive to the phishing attacks, playing catch-up to the phishers’ evolving strategies is not an option. Machine Learning techniques play a significant role in developing effective anti-phishing models. This paper looks at phishing as a classification problem and outlines some of the recent intelligent machine learning techniques (associative classifications, dynamic self-structuring neural network, dynamic rule-induction, etc.) in the literature that is used as anti-phishing models. The purpose of this review is to serve researchers, organisations’ managers, computer security experts, lecturers, and students who are interested in understanding phishing and its corresponding intelligent solutions. This will equip individuals with knowledge and skills that may prevent phishing on a wider context within the community.

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Rubenstein, William J., Sachin Allahabadi, Frank Curriero, Brian T. Feeley, and Drew A. Lansdown. "Fracture Epidemiology in Professional Baseball From 2011 to 2017." Orthopaedic Journal of Sports Medicine 8, no. 8 (August 1, 2020): 232596712094316. http://dx.doi.org/10.1177/2325967120943161.

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Background: Fractures are a significant cause of missed time in Major League Baseball (MLB) and Minor League Baseball (MiLB). MLB and the MLB Players Association recently instituted rule changes to limit collisions at home plate and second base. Purpose: To evaluate the epidemiologic characteristics of fractures in professional baseball and to assess the change in acute fracture incidence secondary to traumatic collisions at home plate and second base after the recently instituted rule changes. Study Design: Descriptive epidemiology study. Methods: The MLB Health and Injury Tracking System (HITS) database was used to access injury information on MLB and MiLB players to analyze fracture data from 2011 to 2017. Injuries were included if the primary diagnosis was classified as a fracture in the HITS system in its International Classification of Diseases, Ninth Revision, codes; injuries were excluded if they were not work related, if they occurred in the offseason, or if they were sustained by a nonplayer. The proportion of fractures occurring due to contact with the ground or another person in the relevant area of the field—home plate or second base—in the years before rule implementation was compared with the years after. Results: A total of 1798 fractures were identified: 342 among MLB players and 1456 among MiLB players. Mean time missed per fracture was 56.6 ± 48.4 days, with significantly less time missed in MLB (46.8 ± 47.7 days) compared with MiLB (59.0 ± 48.3 days) ( P < .0001). A 1-way analysis of variance with post hoc Bonferroni correction demonstrated that starting pitchers missed significantly more time due to fractures per injury than all other position groups ( P < .0001). Acute fractures due to contact with the ground or with another athlete were significantly decreased after rule implementation at home plate in 2014 (22 [3.0%] vs 14 [1.3%]; P = .015) and at second base in 2016 (90 [7.0%] vs 23 [4.5%]; P = .045). Conclusion: The recently instituted rule changes to reduce collisions between players at home plate and at second base are associated with reductions in the proportion of acute fractures in those areas on the field.

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Gomathy, B., S. M. Ramesh, and A. Shanmugam. "A Hybrid Model for Enhanced Prediction of Medical Diagnosis Based on Discriminative Rule Framing and Correlated Framework Approaches." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, no. 01 (February 2015): 33–48. http://dx.doi.org/10.1142/s0218488515500026.

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Medical diagnosis is mostly done by experienced doctors. However, still some of the cases reported of wrong diagnosis and treatment. Patients are needed to take number of clinical tests for disease diagnosis. Most of the cases, all the tests are not contributing towards efficient diagnosis. The medical data are multidimensional and composed of thousands of independent features. So, the multidimensional database need to be analyzed and preprocessed for valuable decision making for medical diagnosis. The aim of this work is to accurately predict the medical disease with a condensed number of attributes. In this approach, the raw input dataset is preprocessed based on the common normalization approach. An association rule is used to find out the frequent used patterns to prune the dataset. Further, base rule can be applied to the pruned dataset. The Payoff and Heuristic rate can be evaluated to predict the risk analysis. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) approaches are used for better feature selection. Classification result is acquired based on minimum and maximum of residual support values. The experimental results show that the proposed scheme, can perform better than the existing algorithms to diagnose the medical disease.

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Lukáš Brodský and Luboš, Borůvka. "Object-oriented Fuzzy Analysis of Remote Sensing Data for Bare Soil Brightness Mapping." Soil and Water Research 1, No. 3 (January 7, 2013): 79–84. http://dx.doi.org/10.17221/6509-swr.

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Remote sensing data have an important advantage; the data provide spatially exhaustive sampling of the area of interest instead of having samples of tiny fractions. Vegetation cover is, however, one of the application constraints in soil science. Areas of bare soil can be mapped. These spatially dense data require proper techniques to map identified patterns. The objective of this study was mapping of spatial patterns of bare soil colour brightness in a Landsat 7 satellite image in the study area of Central Bohemia using object-oriented fuzzy analysis. A soil map (1:200 000) was used to associate soil types with the soil brightness in the image. Several approaches to determine membership functions (MF) of the fuzzy rule base were tested. These included a simple manual approach, k-means clustering, a method based on the sample histogram, and one using the probability density function. The method that generally provided the best results for mapping the soil brightness was based on the probability density function with KIA = 0.813. The resulting classification map was finally compared with an existing soil map showing 72.0% agreement of the mapped area. The disagreement of 28.0% was mainly in the areas of Chernozems (69.3%).

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Thanajiranthorn, Chartwut, and Panida Songram. "Efficient Rule Generation for Associative Classification." Algorithms 13, no. 11 (November 17, 2020): 299. http://dx.doi.org/10.3390/a13110299.

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Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances. AC is one of the effective classification techniques that applies the generated rules to perform classification. In particular, the number of frequent ruleitems generated by AC is inherently designated by the degree of certain minimum supports. A low minimum support can potentially generate a large set of ruleitems. This can be one of the major drawbacks of AC when some of the ruleitems are not used in the classification stage, and thus (to reduce the rule-mapping time), they are required to be removed from the set. This pruning process can be a computational burden and massively consumes memory resources. In this paper, a new AC algorithm is proposed to directly discover a compact number of efficient rules for classification without the pruning process. A vertical data representation technique is implemented to avoid redundant rule generation and to reduce time used in the mining process. The experimental results show that the proposed algorithm archives in terms of accuracy a number of generated ruleitems, classifier building time, and memory consumption, especially when compared to the well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).

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THABTAH, FADI. "A review of associative classification mining." Knowledge Engineering Review 22, no. 1 (March 2007): 37–65. http://dx.doi.org/10.1017/s0269888907001026.

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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.

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Abdelhamid, Neda, and Fadi Thabtah. "Associative Classification Approaches: Review and Comparison." Journal of Information & Knowledge Management 13, no. 03 (September 2014): 1450027. http://dx.doi.org/10.1142/s0219649214500270.

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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.

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BEAUSOLEIL, RICARDO P. "ASSOCIATIVE CLASSIFICATION WITH MULTIOBJECTIVE TABU SEARCH." Revista de Matemática: Teoría y Aplicaciones 27, no. 2 (June 23, 2020): 353–74. http://dx.doi.org/10.15517/rmta.v27i2.42438.

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This paper presents an application of Tabu Search algorithm to association rule mining. We focus our attention specifically on classification rule mining, often called associative classification, where the consequent part of each rule is a class label. Our approach is based on seek a rule set handled as an individual. A Tabu search algorithm is used to search for Pareto-optimal rule sets with respect to some evaluation criteria such as accuracy and complexity. We apply a called Apriori algorithm for an association rules mining and then a multiobjective tabu search to a selection rules. We report experimental results where the effect of our multiobjective selection rules is examined for some well-known benchmark data sets from the UCI machine learning repository.

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Thabtah, Fadi. "Rule Preference Effect in Associative Classification Mining." Journal of Information & Knowledge Management 05, no. 01 (March 2006): 13–20. http://dx.doi.org/10.1142/s0219649206001281.

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Classification based on association rule mining, also known as associative classification, is a promising approach in data mining that builds accurate classifiers. In this paper, a rule ranking process within the associative classification approach is investigated. Specifically, two common rule ranking methods in associative classification are compared with reference to their impact on accuracy. We also propose a new rule ranking procedure that adds more tie breaking conditions to the existing methods in order to reduce rule random selection. In particular, our method looks at the class distribution frequency associated with the tied rules and favours those that are associated with the majority class. We compare the impact of the proposed rule ranking method and two other methods presented in associative classification against 14 highly dense classification data sets. Our results indicate the effectiveness of the proposed rule ranking method on the quality of the resulting classifiers for the majority of the benchmark problems, which we consider. This provides evidence that adding more appropriate constraints to break ties between rules positively affects the predictive power of the resulting associative classifiers.

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Karyawati, Eka, and Edi Winarko. "Class Association Rule Pada Metode Associative Classification." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 5, no. 3 (November 19, 2011): 17. http://dx.doi.org/10.22146/ijccs.5207.

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Frequent patterns (itemsets) discovery is an important problem in associative classification rule mining. Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and Transaction Data Location (Tid)-list Intersection algorithm. This paper focuses on surveying and comparing the state of the art associative classification techniques with regards to the rule generation phase of associative classification algorithms. This phase includes frequent itemsets discovery and rules mining/extracting methods to generate the set of class association rules (CARs). There are some techniques proposed to improve the rule generation method. A technique by utilizing the concepts of discriminative power of itemsets can reduce the size of frequent itemset. It can prune the useless frequent itemsets. The closed frequent itemset concept can be utilized to compress the rules to be compact rules. This technique may reduce the size of generated rules. Other technique is in determining the support threshold value of the itemset. Specifying not single but multiple support threshold values with regard to the class label frequencies can give more appropriate support threshold value. This technique may generate more accurate rules. Alternative technique to generate rule is utilizing the vertical layout to represent dataset. This method is very effective because it only needs one scan over dataset, compare with other techniques that need multiple scan over dataset. However, one problem with these approaches is that the initial set of tid-lists may be too large to fit into main memory. It requires more sophisticated techniques to compress the tid-lists.

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Abdelhamid, Neda, Aladdin Ayesh, Fadi Thabtah, Samad Ahmadi, and Wael Hadi. "MAC: A Multiclass Associative Classification Algorithm." Journal of Information & Knowledge Management 11, no. 02 (June 2012): 1250011. http://dx.doi.org/10.1142/s0219649212500116.

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Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers). Several research studies reveal that AC normally generates higher accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and decision trees. This paper proposes a new multiclass AC algorithm called MAC. The proposed algorithm employs a novel method for building the classifier that normally reduces the resulting classifier size in order to enable end-user to more understand and maintain it. Experimentations against 19 different data sets from the UCI data repository and using different common AC and traditional learning approaches have been conducted with reference to classification accuracy and the number of rules derived. The results show that the proposed algorithm is able to derive higher predictive classifiers than rule induction (RIPPER) and decision tree (C4.5) algorithms and very competitive to a known AC algorithm named MCAR. Furthermore, MAC is also able to produce less number of rules than MCAR in normal circumstances (standard support and confidence thresholds) and in sever circumstances (low support and confidence thresholds) and for most of the data sets considered in the experiments.

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THABTAH, FADI, WAEL HADI, NEDA ABDELHAMID, and AYMAN ISSA. "PREDICTION PHASE IN ASSOCIATIVE CLASSIFICATION MINING." International Journal of Software Engineering and Knowledge Engineering 21, no. 06 (September 2011): 855–76. http://dx.doi.org/10.1142/s0218194011005463.

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Associative classification (AC) is an important data mining approach which effectively integrates association rule mining and classification. Prediction of test data is a fundamental step in classification that impacts the outputted system accuracy. In this paper, we present three new prediction methods (Dominant Class Label, Highest Average Confidence per Class, Full Match Rule) and one rule pruning procedure (Partial Matching) in AC. Furthermore, we review current prediction methods in AC. Experimental results on large English and Arabic text categorisation data collections (Reuters, SPA) using the proposed prediction methods and other popular classification algorithms (SVM, KNN, NB, BCAR, MCAR, C4.5, etc.), have been conducted. The bases of the comparison in the experiments are classification accuracy and the Break-Even-Point (BEP) evaluation measures. The results reveal that our prediction methods are very competitive with reference to BEP if compared with known AC prediction approaches such as those of 2-PS, ARC-BC and BCAR. Moreover, the proposed prediction methods outperform other existing methods in traditional classification approaches such as decision trees, and probabilistic with regards to accuracy. Finally, the results indicate that using the proposed pruning procedure in AC improved the accuracy of the outputted classifier.

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Pal, Parashu Ram, Pankaj Pathak, and Shkurte Luma-Osmani. "IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification." Journal of Information & Knowledge Management 20, no. 01 (March 2021): 2150010. http://dx.doi.org/10.1142/s0219649221500106.

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Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

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Geng, Xiaojiao, Yan Liang, and Lianmeng Jiao. "EARC: Evidential association rule-based classification." Information Sciences 547 (February 2021): 202–22. http://dx.doi.org/10.1016/j.ins.2020.07.067.

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Zhai, Yue, Xi Yu, and An Sheng Deng. "Class Association Rule Mining Based on Incremental Construction of Lattice." Applied Mechanics and Materials 571-572 (June 2014): 345–50. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.345.

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In the problem of mining classification rules, previous methods are too general or too over-fitting for a given dataset. In this paper, through analyzing the characteristics of concept lattice and indiscernible matrix, classification rule acquisition based on lattice was proposed. Our method includes two phase: (1) proposing incremental generating the nodes of lattice using indiscernible matrix. (2) Developing some theorems for pruning redundant rules quickly. It is shown by experimental results that our approach not only results in shorter execution times, but also avoids missing important rules than the generalization of previous known methods.

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Odeh, Fadi, and Nijad Al-Najdawi. "ACNB." International Journal of Information Technology and Web Engineering 8, no. 1 (January 2013): 23–35. http://dx.doi.org/10.4018/jitwe.2013010102.

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Integrating association rule discovery and classification in data mining brings a new approach known as associative classification. Associative classification is a promising approach that often constructs more accurate classification models (classifiers) than the traditional classification approaches such as decision trees and rule induction. In this research, the authors investigate the use of associative classification on the high dimensional data in text categorization. This research focuses on prediction, a very important step in classification, and introduces a new prediction method called Associative Classification Mining based on Naïve Bayesian method. The running time is decreased by removing the ranking procedure that is usually the first step in ranking the derived Classification Association Rules. The prediction method is enhanced using the Naïve Bayesian Algorithm. The results of the experiments demonstrate high classification accuracy.

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Qi, Wei Qiang, Yan Ran Li, Hai Feng Ye, Da Peng Duan, and Xiu Chen Jiang. "Research on Classification of Partial Discharge of Switchgear Cabinets Based on a Novel Association Rule Algorithm." Applied Mechanics and Materials 448-453 (October 2013): 3485–93. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3485.

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In order to assess switchgear insulation status, a novel association rule mining (ARM) algorithm is presented. It is used to recognize the severity of switchgear cabinet partial discharge. The algorithm uses fuzzy C-means clustering (FCM) to divide partial discharge feature interval, candidate sets meeting minimum support and minimum confidence are sought based on an improved Apriori algorithm. Multiple recursions and scans are performed on candidate sets to generate association rules library for classification. Fuzzy reasoning based on association rules are performed over multiple needle corona partial discharge signals sampled in 10KV switchgear cabinets. The results show that partial discharge classification rate using association rules is high and classification conclusions are accurate. It has provided theoretical basis and practical value for insulation status assessment of switchgear cabinets.

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Refai. "PARTIAL RULE MATCH FOR FILTERING RULES IN ASSOCIATIVE CLASSIFICATION." Journal of Computer Science 10, no. 4 (April 1, 2014): 570–77. http://dx.doi.org/10.3844/jcssp.2014.570.577.

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Mohd Shaharanee, Izwan Nizal, and Jastini Mohd Jamil. "IRRELEVANT FEATURE AND RULE REMOVAL FOR STRUCTURAL ASSOCIATIVE CLASSIFICATION." Journal of Information and Communication Technology 14 (April 2015): 95–110. http://dx.doi.org/10.32890/jict2015.14.6.

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Arunadevi, J., and V. Rajamani. "An Evolutionary Multi Label Classification Using Associative Rule Mining." International Journal of Soft Computing 6, no. 2 (February 1, 2011): 20–25. http://dx.doi.org/10.3923/ijscomp.2011.20.25.

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Kang, Wan Li, Jing Jing Wu, Du Wu Cui, and Li Zhao. "An Oriented Clonal Selection Algorithm for Associative Classification." Applied Mechanics and Materials 170-173 (May 2012): 3320–23. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3320.

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In this paper, we present an oriented clonal selection algorithm (O-CLONALG) for mining association rules effectively for classification. Different with the traditional evolutionary algorithms, O-CLONALG firstly scans dataset one time to find the frequent rules with one item. The items are used to generate new rules and the mutation operation is limited in it. When mutation operation takes place, each rule in the same generation was added a new item. The results have shown that it is efficient in dealing with the problem on the complexity of the rule search space. At the same time, good classification accuracy has been achieved

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Li, Tianyu, Fangyan Dong, and Kaoru Hirota. "Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (March 20, 2015): 217–24. http://dx.doi.org/10.20965/jaciii.2015.p0217.

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A fuzzy association rule mining based method is proposed for myocardial ischemia diagnosis on ECG signals. The proposal provides interpretable and understandable information to doctors as an assistant reference, while rule mining on fuzzy itemsets guarantees that the feature segmentation before rule extraction is feasible and effective. A set of fuzzy association rules is mined through experiments on data from the European ST-T Database, and classification results of myocardial ischemia and normal heartbeats on the test dataset using the extracted rules obtained values of 83.4%, 80.7%, and 81.4% for sensitivity, specificity, and accuracy, respectively. The proposed method aims to become a helpful tool to accelerate the diagnosis of myocardial ischemia on ECG signal, and to be expanded to other heart disease diagnosis areas such as hypertensive heart disease and arrhythmia.

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Zhang, Zhonglin, Zongcheng Liu, and Chongyu Qiao. "Tendency Mining in Dynamic Association Rules Based on SVM Classifier." Open Mechanical Engineering Journal 8, no. 1 (September 16, 2014): 303–7. http://dx.doi.org/10.2174/1874155x01408010303.

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A method of tendency mining in dynamic association rule based on compatibility feature vector SVM classifier is proposed. Firstly, the class association rule set named CARs is mined by using the method of tendency mining in dynamic association rules. Secondly, the algorithm of SVM is used to construct the classifier based on compatibility feature vector to classify the obtained CARs taking advantage when dealing with high complex data. It uses a method based on judging rules’ weight to construct the model. At last, the method is compared with the traditional methods with respect to the mining accuracy. The method can solve the problem of high time complexity and have a higher accuracy than the traditional methods which is helpful to make mining dynamic association rules more accurate and effective. By analyzing the final results, it is proved that the method has lower complexity and higher classification accuracy.

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Lu, Nannan, Shingo Mabu, and Kotaro Hirasawa. "Integrated Rule Mining Based on Fuzzy GNP and Probabilistic Classification for Intrusion Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 5 (July 20, 2011): 495–505. http://dx.doi.org/10.20965/jaciii.2011.p0495.

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With the increasing popularity of the Internet, network security has become a serious problem recently. How to detect intrusions effectively becomes an important component in network security. Therefore, a variety of algorithms have been devoted to this challenge. Genetic network programming is a newly developed evolutionary algorithm with directed graph gene structures, and it has been applied to data mining for intrusion detection systems providing good performances in intrusion detection. In this paper, an integrated rule mining algorithm based on fuzzy GNP and probabilistic classification is proposed. The integrated rule mining uses fuzzy class association rule mining algorithm to extract rules with different classes. Actually, it can deal with both discrete and continuous attributes in network connection data. Then, the classification is done probabilistically using different class rules. The integrated method showed excellent results by simulation experiments.

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Ochin, Suresh Kumar, and Nisheeth Joshi. "Rule Power Factor: A New Interest Measure in Associative Classification." Procedia Computer Science 93 (2016): 12–18. http://dx.doi.org/10.1016/j.procs.2016.07.175.

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Mishra, Meenakshi, and Santosh K. "Text Classification based on Association Rule Mining Technique." International Journal of Computer Applications 169, no. 10 (July 17, 2017): 46–50. http://dx.doi.org/10.5120/ijca2017914905.

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Thabtah, F. A., and P. I. Cowling. "A greedy classification algorithm based on association rule." Applied Soft Computing 7, no. 3 (June 2007): 1102–11. http://dx.doi.org/10.1016/j.asoc.2006.10.008.

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Lin, Lin, Mei-Ling Shyu, and Shu-Ching Chen. "Rule-Based Semantic Concept Classification from Large-Scale Video Collections." International Journal of Multimedia Data Engineering and Management 4, no. 1 (January 2013): 46–67. http://dx.doi.org/10.4018/jmdem.2013010103.

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The explosive growth and increasing complexity of the multimedia data have created a high demand of multimedia services and applications in various areas so that people can access and distribute the data easily. Unfortunately, traditional keyword-based information retrieval is no longer suitable. Instead, multimedia data mining and content-based multimedia information retrieval have become the key technologies in modern societies. Among many data mining techniques, association rule mining (ARM) is considered one of the most popular approaches to extract useful information from multimedia data in terms of relationships between variables. In this paper, a novel rule-based semantic concept classification framework using weighted association rule mining (WARM), capturing the significance degrees of the feature-value pairs to improve the applicability of ARM, is proposed to deal with major issues and challenges in large-scale video semantic concept classification. Unlike traditional ARM that the rules are generated by frequency count and the items existing in one rule are equally important, our proposed WARM algorithm utilizes multiple correspondence analysis (MCA) to explore the relationships among features and concepts and to signify different contributions of the features in rule generation. To the authors best knowledge, this is one of the first WARM-based classifiers in the field of multimedia concept retrieval. The experimental results on the benchmark TRECVID data demonstrate that the proposed framework is able to handle large-scale and imbalanced video data with promising classification and retrieval performance.

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Shaout, Adnan, and Juan C. Garcia. "Fuzzy Rule Base System for Software Classification." International Journal of Computer Science and Information Technology 5, no. 3 (June 30, 2013): 1–21. http://dx.doi.org/10.5121/ijcsit.2013.5301.

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Jain, Deepti, and Divakar Singh. "A Review on associative classification for Diabetic Datasets A Simulation Approach." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (May 21, 2013): 533–38. http://dx.doi.org/10.24297/ijct.v7i1.3483.

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Association rules are used to discover all the interesting relationship in a potentially large database. Association rule mining is used to discover a small set of rules over the database to form more accurate evaluation. They capture all possible rules that explain the presence of some attributes in relation to the presence of other attributes. This review paper aims to study and observe a flexible way, of, mining frequent patterns by extending the idea of the Associative Classification method. For better performance, the Neural Network Association Classification system is also analyzed here to be one of the approaches for building accurate and efficient classifiers. In this review paper, the Neural Network Association Classification system is studied and compared in order to find best possible accurate results. Association rule mining and classification rule mining can be integrated to form a framework called as Associative Classification and these rules are referred as Class Association Rules. This review research paper also analyzes how data mining techniques are used for predicting different types of diseases. This paper reviewed the research papers which mainly concentrated on predicting Diabetes.

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Thabtah, Fadi, Peter Cowling, and Suhel Hammoud. "Improving rule sorting, predictive accuracy and training time in associative classification." Expert Systems with Applications 31, no. 2 (August 2006): 414–26. http://dx.doi.org/10.1016/j.eswa.2005.09.039.

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Mohd Shaharanee, Izwan Nizal, and Jastini Jamil. "Evaluation And Optimization Of Frequent Association Rule Based Classification." Asia-Pacific Journal of Information Technology and Multimedia 03, no. 01 (June 15, 2014): 1–13. http://dx.doi.org/10.17576/apjitm-2014-0301-01.

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K.Shinde, Ankita, and Pramod B. Mali. "Classification based on Predictive Association Rule for Discrimination Prevention." International Journal of Computer Applications 85, no. 19 (January 16, 2014): 14–17. http://dx.doi.org/10.5120/15094-3339.

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Journal articles on the topic 'Associative classification rule base'

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