Journal articles: 'Associative Rules'

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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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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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VERGINI, EDUARDO G., and MARCELO G. BLATT. "LEARNING RULES FOR ASSOCIATIVE MEMORIES." Modern Physics Letters B 05, no. 30 (December 30, 1991): 1963–72. http://dx.doi.org/10.1142/s0217984991002367.

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We discuss some of the most popular learning rules that can be used to construct Neural Networks that act as associative memories. The Hebb’s rule, perceptron type algorithms and the projector rule with local versions are included.

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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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Shimada, Kaoru, and Takashi Hanioka. "An Evolutionary Method for Associative Contrast Rule Mining from Incomplete Database." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 766–77. http://dx.doi.org/10.20965/jaciii.2015.p0766.

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We propose a method for associative contrast rule mining from an incomplete database to find interesting differences between two incomplete datasets. The associative contrast rule is defined as follows: although an association rule “if X then Y” satisfies the given importance conditions within Database A, the same rule does not satisfy the same conditions within Database B. The proposed method extracts associative contrast rules directly without generating the frequent itemsets used in conventional rule mining methods. We developed our message using the basic evolutionary graph-based optimization basic structure and a new evolutionary strategy for rule accumulation mechanism. The method realizes association analysis between two classes of an incomplete database using the chi-square test. We evaluated the performance of the method for associative contrast rule mining from the incomplete database. Experimental results showed that our proposed method extracts associative contrast rules effectively. Evaluations of the mischief for rule measurements by missing values are demonstrated. Simulation results showed the difference between using the proposed method for an incomplete database and using the database as complete.

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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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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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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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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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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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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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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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Chen, Zuoliang, and Guoqing Chen. "Building an Associative Classifier Based on Fuzzy Association Rules." International Journal of Computational Intelligence Systems 1, no. 3 (2008): 262. http://dx.doi.org/10.2991/ijcis.2008.1.3.7.

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Chen, Zuoliang, and Guoqing Chen. "Building an Associative Classifier Based on Fuzzy Association Rules." International Journal of Computational Intelligence Systems 1, no. 3 (2008): 262. http://dx.doi.org/10.2991/jnmp.2008.1.3.7.

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Chen, Zuoliang, and Guoqing Chen. "Building an Associative Classifier Based on Fuzzy Association Rules." International Journal of Computational Intelligence Systems 1, no. 3 (August 2008): 262–73. http://dx.doi.org/10.1080/18756891.2008.9727623.

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Thomas, Binu, and G. Raju. "A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules." ISRN Artificial Intelligence 2013 (December 19, 2013): 1–10. http://dx.doi.org/10.1155/2013/316913.

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In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm.

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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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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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Биллиг, В. А., and V. A. Billig. "Effective algorithm for constructing associative rules." Международный журнал "Программные продукты и системы" 23 (May 26, 2017): 196–206. http://dx.doi.org/10.15827/0236-235x.118.196-206.

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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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Mohd Khairudin, Nazli, Aida Mustapha, and Mohd Hanif Ahmad. "Effect of Temporal Relationships in Associative Rule Mining for Web Log Data." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/813983.

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The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.

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Mardiaha, Ainul, and Yulia Yulia. "Implementasi Data Mining Menggunakan Algoritma Apriori Pada Penjualan Suku Cadang Motor." Jurnal Ilmu Komputer 14, no. 2 (September 30, 2021): 125. http://dx.doi.org/10.24843/jik.2021.v14.i02.p07.

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This research was carried out to simplify or assist Candra Motor workshop owners in managing data and archives of motorcycle parts sales by applying a data mining a priori algorithm method. Data mining is an operation that uses a particular technique or method to look for different patterns or shapes in a selected data. Sales data for a year with the number of 15 items selected using the priori algorithm method. A priori algorithm is an algorithm for taking data with associative rules (association rule) to determine the associative relationship of an item combination. In a priori algorithm, it is determined frequent itemset-1, frequent itemset-2, and frequent itemset-3 so that the association rules can be obtained from previously selected data. To obtain the frequent itemset, each selected data must meet the minimum support and minimum confidence requirements. In this study using minimum support ? 7 or 0.583 and minimum confidence of 90%. So that some rules of association were obtained, where the calculation of the search for association rules manually and using WEKA software obtained the same results.By fulfilling the minimum support and minimum confidence requirements, the most sold spare parts are inner tube, Yamaha oil and MPX oil.

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Soni, Sunita, and O. P. Vyas. "Building Weighted Associative Classifiers using Maximum Likelihood Estimation to Improve Prediction Accuracy in Health Care Data Mining." Journal of Information & Knowledge Management 12, no. 01 (March 2013): 1350008. http://dx.doi.org/10.1142/s0219649213500081.

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Associative classifiers are new classification approach that use association rules for classification. An important advantage of these classification systems is that, using association rule mining (ARM) they are able to examine several features at a time. Many applications can benefit from good classification model. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Medical diagnosis is a domain where the maximum accuracy of the model is desired. In this paper, we propose a framework weighted associative classifier (WAC) that assigns different weights to different attributes according to their predicting capability. We are using maximum likelihood estimation (MLE) theory to calculate weight of each attribute using training data. We also show how existing Apriori algorithm can be modified in weighted environment to infer association rule from medical dataset having numeric valued attributes as the conventional ARM usually deals with the transaction database with categorical values. Experiments have been performed on benchmark data set to evaluate the performance of WAC in terms of accuracy, number of rules generating and impact of minimum support threshold on WAC outcomes. The result reveals that WAC is a promising alternative in medical prediction and certainly deserves further attention.

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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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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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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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Malaterre, Christophe, Jean-François Chartier, and Francis Lareau. "The recipes of Philosophy of Science: Characterizing the semantic structure of corpora by means of topic associative rules." PLOS ONE 15, no. 11 (November 18, 2020): e0242353. http://dx.doi.org/10.1371/journal.pone.0242353.

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Scientific articles have semantic contents that are usually quite specific to their disciplinary origins. To characterize such semantic contents, topic-modeling algorithms make it possible to identify topics that run throughout corpora. However, they remain limited when it comes to investigating the extent to which topics are jointly used together in specific documents and form particular associative patterns. Here, we propose to characterize such patterns through the identification of “topic associative rules” that describe how topics are associated within given sets of documents. As a case study, we use a corpus from a subfield of the humanities—the philosophy of science—consisting of the complete full-text content of one of its main journals: Philosophy of Science. On the basis of a pre-existing topic modeling, we develop a methodology with which we infer a set of 96 topic associative rules that characterize specific types of articles depending on how these articles combine topics in peculiar patterns. Such rules offer a finer-grained window onto the semantic content of the corpus and can be interpreted as “topical recipes” for distinct types of philosophy of science articles. Examining rule networks and rule predictive success for different article types, we find a positive correlation between topological features of rule networks (connectivity) and the reliability of rule predictions (as summarized by the F-measure). Topic associative rules thereby not only contribute to characterizing the semantic contents of corpora at a finer granularity than topic modeling, but may also help to classify documents or identify document types, for instance to improve natural language generation processes.

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Ksiksi, A., and H. Amiri. "Using Association Rules to Enrich Arabic Ontology." Engineering, Technology & Applied Science Research 8, no. 3 (June 19, 2018): 2914–18. http://dx.doi.org/10.48084/etasr.1998.

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In this article, we propose the use of a minimal generic base of associative rules between term association rules, to automatically enrich an existing domain ontology. Initially, non-redundant association rules between terms are extracted from an Arabic corpus. Then, the matching of the candidate terms is done through the matching between the concepts of the initial ontology and the premises of the association rules, with three distance measures that we define.

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Kim, Hyun-Jin, Ji-Won Baek, and Kyungyong Chung. "Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score." Applied Sciences 10, no. 13 (July 2, 2020): 4590. http://dx.doi.org/10.3390/app10134590.

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This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are generated. News data are first collected through crawling and then are converted into a corpus through preprocessing. Unnecessary data are removed through preprocessing including lowercase conversion, removal of punctuation marks and stop words. In the document term matrix, words are extracted and then transactions are generated. In the data cleaning process, the Apriori algorithm is applied to generate association rules and make a knowledge graph. To optimize the generated knowledge graph, the proposed method utilizes TF-IDF based ranking scores to remove terms with low scores and recreate transactions. Based on the result, the association rule algorithm is applied to create an optimized knowledge model. The performance is evaluated in rule generation speed and usefulness of association rules. The association rule generation speed of the proposed method is about 22 seconds faster. And the lift value of the proposed method for usefulness is about 0.43 to 2.51 higher than that of each one of conventional association rule algorithms.

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Hichem, Haouassi, MEHDAOUI Rafik, and Chouhal Ouahiba. "New Discrete Crow Search Algorithm for Class Association Rule Mining." International Journal of Swarm Intelligence Research 13, no. 1 (January 2022): 1–21. http://dx.doi.org/10.4018/ijsir.2022010109.

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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.

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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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Dami, Asmae, Mohamed Fakir, and Belaid Bouikhalene. "Information Retrieval (IR) and Extracting Associative Rules." Journal of Information Technology Research 7, no. 4 (October 2014): 42–62. http://dx.doi.org/10.4018/jitr.2014100104.

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This paper is located in the intersection of two research themes, namely: Information Retrieval and Knowledge Discovery from texts (Text mining). The purpose of this paper is two-fold: first, it focuses on Information Retrieval (IR) whose purpose is to implement a set of models and systems for selecting a set of documents satisfying user needs in terms of information expressed as a query. An information retrieval system is composed mainly of two processes the representation and retrieval process. The process of representation is called indexing, which allows representation of documents and queries by descriptors, or indexes. These descriptors reflect the contents of documents. The retrieval process consists on the comparison between documents representations and query representation. The second aim of this paper is to discover the relationships between terms (keywords) descriptors of documents in a document database. The correlations (relationships) between terms are extracted by using a technique of the Text mining, mainly association rules.

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Natwichai, Juggapong, Xingzhi Sun, and Xue Li. "Associative classification rules hiding for privacy preservation." International Journal of Intelligent Information and Database Systems 5, no. 3 (2011): 246. http://dx.doi.org/10.1504/ijiids.2011.040088.

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Dua, Sumeet, Harpreet Singh, and H. W. Thompson. "Associative classification of mammograms using weighted rules." Expert Systems with Applications 36, no. 5 (July 2009): 9250–59. http://dx.doi.org/10.1016/j.eswa.2008.12.050.

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Ozawa, Takaaki, and Joshua P. Johansen. "Learning rules for aversive associative memory formation." Current Opinion in Neurobiology 49 (April 2018): 148–57. http://dx.doi.org/10.1016/j.conb.2018.02.010.

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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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Behr, Nicolas, and Jean Krivine. "Compositionality of Rewriting Rules with Conditions." Compositionality 3 (April 22, 2021): 2. http://dx.doi.org/10.32408/compositionality-3-2.

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We extend the notion of compositional associative rewriting as recently studied in the rule algebra framework literature to the setting of rewriting rules with conditions. Our methodology is category-theoretical in nature, where the definition of rule composition operations encodes the non-deterministic sequential concurrent application of rules in Double-Pushout (DPO) and Sesqui-Pushout (SqPO) rewriting with application conditions based upon M-adhesive categories. We uncover an intricate interplay between the category-theoretical concepts of conditions on rules and morphisms, the compositionality and compatibility of certain shift and transport constructions for conditions, and thirdly the property of associativity of the composition of rules.

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Ren, Jian Si. "Research and Implementation of Text Classification Algorithm." Applied Mechanics and Materials 644-650 (September 2014): 2395–98. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2395.

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The development of Internet and digital library has triggered a lot of text categorization methods. How to find desired information accurately and timely is becoming more and more important and automatic text categorization can help us achieve this goal. In general, text classifier is implemented by using some traditional classification methods such as Naive-Bayes (NB). ARC-BC (Associative Rule-based Classifier by Category) can be used for text categorization by dividing text documents into subsets in which all documents belong to the same category and generate associative classification rules for each subset. This classifier differs from previous methods in that it consists of discovered association rules between words and categories extracted from the training set. In order to train and test this classifier, we constructed training data and testing data respectively by selecting documents from Yahoo. The experimental result shows that the performance of ARC-BC based text categorization is very pretty efficient and effective and it is comparable to Naïve Bayesian algorithm based text categorization.

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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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Li, Jundong, and Osmar R. Zaiane. "Exploiting statistically significant dependent rules for associative classification." Intelligent Data Analysis 21, no. 5 (October 10, 2017): 1155–72. http://dx.doi.org/10.3233/ida-163141.

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Cherenkov, Igor. "FORECASTING BASED ON THE NEWSLETTER THROUGH ASSOCIATIVE RULES." Energy saving. Power engineering. Energy audit., no. 1(151) (December 18, 2020): 70–75. http://dx.doi.org/10.20998/2413-4295.2020.02.08.

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Dayan, P., and D. J. Willshaw. "Optimising synaptic learning rules in linear associative memories." Biological Cybernetics 65, no. 4 (August 1991): 253–65. http://dx.doi.org/10.1007/bf00206223.

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Chen, Yen-Liang, and Lucas Tzu-Hsuan Hung. "Using decision trees to summarize associative classification rules." Expert Systems with Applications 36, no. 2 (March 2009): 2338–51. http://dx.doi.org/10.1016/j.eswa.2007.12.031.

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Dhahir, Al-Mahmood Ahmed Wasfi, and Luayabdul wahidshihab. "USING АPRIORI ALGORITHM TO SEARCH FOR ASSOCIATIVE RULES." International Journal of Business Management and Economic Review 04, no. 02 (2021): 22–30. http://dx.doi.org/10.35409/ijbmer.2021.3237.

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Parasuraman, S., and Bijan Shirinzadeh. "Fuzzy Logic Based Sensors Data Fusion for Mobile Robot Navigation." Key Engineering Materials 467-469 (February 2011): 794–99. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.794.

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In this paper, a sensor data fusion technique is proposed to develop the effective robot navigation and optimize the navigation rules using a Modified Fuzzy Associative Memory (MFAM). MFAM provides good flexibility to use multiple input space and reduction of rule base for robot navigation. The behavior rules obtained from MFAM model are tested using the experimental studies and results are discussed and compared with the existing methods.

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Lee, Yuan, and Insin Kim. "Analyzing associative rules of destination selections among foreign tourists : A comparative study of associative rules between leisure and business tourist groups." Journal of Tourism Management Research 20, no. 6 (November 30, 2016): 623–42. http://dx.doi.org/10.18604/tmro.2016.20.6.30.

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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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GANGULY, NILOY, PRADIPTA MAJI, BIPLAB K. SIKDAR, and P. PAL CHAUDHURI. "GENERALIZED MULTIPLE ATTRACTOR CELLULAR AUTOMATA (GMACA) MODEL FOR ASSOCIATIVE MEMORY." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (November 2002): 781–95. http://dx.doi.org/10.1142/s0218001402001988.

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This paper reports an efficient technique of evolving Cellular Automata (CA) as an associative memory model. The evolved CA termed as GMACA (Generalized Multiple Attractor Cellular Automata), acts as a powerful pattern recognizer. Detailed analysis of GMACA rules establishes the fact that the rule subspace of the pattern recognizing CA lies at the edge of chaos — believed to be capable of executing complex computation.

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Chechik, Gal, Isaac Meilijson, and Eytan Ruppin. "Effective Neuronal Learning with Ineffective Hebbian Learning Rules." Neural Computation 13, no. 4 (April 1, 2001): 817–40. http://dx.doi.org/10.1162/089976601300014367.

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In this article we revisit the classical neuroscience paradigm of Hebbian learning. We find that it is difficult to achieve effective associative memory storage by Hebbian synaptic learning, since it requires network-level information at the synaptic level or sparse coding level. Effective learning can yet be achieved even with nonsparse patterns by a neuronal process that maintains a zero sum of the incoming synaptic efficacies. This weight correction improves the memory capacity of associative networks from an essentially bounded one to a memory capacity that scales linearly with network size. It also enables the effective storage of patterns with multiple levels of activity within a single network. Such neuronal weight correction can be successfully carried out by activity-dependent homeostasis of the neuron's synaptic efficacies, which was recently observed in cortical tissue. Thus, our findings suggest that associative learning by Hebbian synaptic learning should be accompanied by continuous remodeling of neuronally driven regulatory processes in the brain.

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Bhukya, Raghuram. "Generalization Driven Fuzzy Classification Rules Extraction using OLAM Data Cubes." International Journal of Engineering and Computer Science 9, no. 2 (February 28, 2020): 24962–69. http://dx.doi.org/10.18535/ijecs/v9i2.4444.

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An fuzzy classification rules extraction model for online analytical mining (OLAM) was explained in this article. The efficient integration of the concept of data warehousing, online analytical processing (OLAP) and data mining systems converges to OLAM results in an efficient decision support system. Even after associative classification proved as most efficient classification technique there is a lack of associative classification proposals in field of OLAM. While most of existing data cube models claims their superiority over other the fuzzy multidimensional data cubes proved to be more intuitive in user perspective and effectively manage data imprecision. Considering these factors, in this paper we propose an associative classification model which can perform classification over fuzzy data cubes. Our method aimed to improve accuracy and intuitive ness of classification model using fuzzy concepts and hierarchical relations. We also proposed a generalization-based criterion for ranking associative classification rules to improve classifier accuracy. The model accuracy tested on UCI standard database.

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Journal articles on the topic 'Associative Rules'

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