Relevant bibliographies by topics / Associative Rules

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Associative Rules'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 May 2022

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Journal articles on the topic "Associative Rules"

1

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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Dissertations / Theses on the topic "Associative Rules"

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Hammoud, Suhel. "MapReduce network enabled algorithms for classification based on association rules." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5833.

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There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters. The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization.
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Abdelhamid, Neda. "Deriving classifiers with single and multi-label rules using new Associative Classification methods." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/10120.

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Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers). The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number of candidate rules, and therefore reducing the classifier size so end-user can easily exploit and maintain it. Moreover, an investigation to both rule ranking and test data classification steps have been conducted in order to improve the performance of AC algorithms in regards to predictive accuracy. Overall, this thesis investigates different problems related to AC not limited to the ones listed above, and the results are new AC algorithms that devise single and multi-label rules from different applications data sets, together with comprehensive experimental results. To be exact, the first algorithm proposed named Multi-class Associative Classifier (MAC): This algorithm derives classifiers where each rule is connected with a single class from a training data set. MAC enhanced the rule discovery, rule ranking, rule filtering and classification of test data in AC. The second algorithm proposed is called Multi-label Classifier based Associative Classification (MCAC) that adds on MAC a novel rule discovery method which discovers multi-label rules from single label data without learning from parts of the training data set. These rules denote vital information ignored by most current AC algorithms which benefit both the end-user and the classifier's predictive accuracy. Lastly, the vital problem related to web threats called 'website phishing detection' was deeply investigated where a technical solution based on AC has been introduced in Chapter 6. Particularly, we were able to detect new type of knowledge and enhance the detection rate with respect to error rate using our proposed algorithms and against a large collected phishing data set. Thorough experimental tests utilising large numbers of University of California Irvine (UCI) data sets and a variety of real application data collections related to website classification and trainer timetabling problems reveal that MAC and MCAC generates better quality classifiers if compared with other AC and rule based algorithms with respect to various evaluation measures, i.e. error rate, Label-Weight, Any-Label, number of rules, etc. This is mainly due to the different improvements related to rule discovery, rule filtering, rule sorting, classification step, and more importantly the new type of knowledge associated with the proposed algorithms. Most chapters in this thesis have been disseminated or under review in journals and refereed conference proceedings.
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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
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Bilal, Kirkici. "Words And Rules In L2 Processing: An Analysis Of The Dual-mechanism Model." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605980/index.pdf.

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The nature of the mental representation and processing of morphologically complex words has constituted one of the major points of controversy in psycholinguistic research over the past two decades. The Dual-Mechanism Model defends the necessity of two separate mechanisms for linguistic processing, an associative memory and a rule-system, which account for the processing of irregular and regular word forms, respectively. The purpose of the present study was to analyse the validity of the claims of the Dual-Mechanism Model for second language (L2) processing in order to contribute to the accumulating but so far equivocal knowledge concerning L2 processing. A second purpose of the study was to find out whether L2 proficiency could be identified as a determining factor in the processing of L2 morphology. Two experiments (a lexical decision task on the English past tense and a elicited production task on English lexical compounds) were run with 22 low-proficiency and 24 high-proficiency first language (L1) Turkish users of L2 English and with 6 L1 speakers of English. The results showed that the regular-irregular dissociation predicted by the Dual-Mechanism Model was clearly evident in the production of English lexical compounds for all three subject groups. A comparatively weaker dissociation coupled with intricate response patterns was found in the processing of the English past tense, though possibly because of a number of confounding factors that were not sufficiently controlled. In addition, direct comparisons of the L2 groups displayed a remarkable effect of L2 proficiency on L2 morphological processing.
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Дрозд, С. А. "Статистично-ймовірнісна оцінка корупційних ризиків в Україні." Master's thesis, Сумський державний університет, 2019. http://essuir.sumdu.edu.ua/handle/123456789/75918.

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У роботі досліджено ймовірність скоєння корупційних злочинів залежно від сфери діяльності та посади можливих злочинців також ймовірність того, що при скоєнні певного корупційного злочину додатково може бути скоєно інший корупційний злочин. Основною метою цього дослідження є розробка моделі статистично-ймовірнісної оцінки корупційних ризиків в Україні.
In the work the probability of Commission of corruption crimes depending on the sphere of activity and a position of possible criminals is investigated also probability of that at Commission of a certain corruption crime in addition other corruption crime can be committed. The main purpose of this study is to develop a model of statistical and probabilistic assessment of corruption risks in Ukraine.
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Lombardini, Alessandro. "Estrazione di Correlazioni Medicali da Social Post non Etichettati con Language Model Neurali e Data Clustering." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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La progressiva informatizzazione della società a cui il mondo contemporaneo sta assistendo, ha generato un radicale cambiamento nelle abitudini delle persone, le quali oggi giorno trascorrono sempre più tempo online e creano reti di conoscenza prima inimmaginabili. Tale cambiamento ha coinvolto, nel suo avanzare, anche gli individui affetti da malattie di varia natura. In particolare, la scarsa disponibilità di informazioni che caratterizza alcuni contesti medici, unita al bisogno di dialogare con altre persone aventi la medesima problematica, ha determinato negli ultimi anni una forte crescita di comunità sulle piattaforme social, all’interno delle quali vengono scambiati dettagli rispetto a trattamenti, centri specializzati e dottori. In questo senso, i social network sono diventati il luogo in cui i pazienti sono più propensi a condividere le proprie esperienze e opinioni maturate durante il corso della propria malattia. Questa tesi nasce dalla consapevolezza del valore di tali dati e dalla volontà di consentire un ragionamento logico deduttivo al di sopra di essi. Nello specifico, si intende estrarre — con un approccio non supervisionato, mediante l’uso di language model neurali e data clustering — le correlazioni semantiche racchiuse nell’elevata quantità di testo generato dagli utenti attraverso interazioni social, prendendo l’Acalasia Esofagea come caso di studio.
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Qing, Yang. "Pruning and summarizing discovered time series association rules." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-31828.

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Sensors are widely used in all aspects of our daily life including factories, hospitals and even our homes. Discovering time series association rules from sensor data can reveal the potential relationship between different sensors which can be used in many applications. However, the time series association rule mining algorithms usually produce rules much more than expected. It’s hardly to under-stand, present or make use of the rules. So we need to prune and summarize the huge amount of rules. In this paper, a two-step pruning method is proposed to reduce both the number and redundancy in the large set of time series rules. Be-sides, we put forward the BIGBAR summarizing method to summarize the rules and present the results intuitively.
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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.
Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
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Koh, Yun Sing, and n/a. "Generating sporadic association rules." University of Otago. Department of Computer Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20070711.115758.

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Association rule mining is an essential part of data mining, which tries to discover associations, relationships, or correlations among sets of items. As it was initially proposed for market basket analysis, most of the previous research focuses on generating frequent patterns. This thesis focuses on finding infrequent patterns, which we call sporadic rules. They represent rare itemsets that are scattered sporadically throughout the database but with high confidence of occurring together. As sporadic rules have low support the minabssup (minimum absolute support) measure was proposed to filter out any rules with low support whose occurrence is indistinguishable from that of coincidence. There are two classes of sporadic rules: perfectly sporadic and imperfectly sporadic rules. Apriori-Inverse was then proposed for perfectly sporadic rule generation. It uses a maximum support threshold and user-defined minimum confidence threshold. This method is designed to find itemsets which consist only of items falling below a maximum support threshold. However imperfectly sporadic rules may contain items with a frequency of occurrence over the maximum support threshold. To look for these rules, variations of Apriori-Inverse, namely Fixed Threshold, Adaptive Threshold, and Hill Climbing, were proposed. However these extensions are heuristic. Thus the MIISR algorithm was proposed to find imperfectly sporadic rules using item constraints, which capture rules with a single-item consequent below the maximum support threshold. A comprehensive evaluation of sporadic rules and current interestingness measures was carried out. Our investigation suggests that current interestingness measures are not suitable for detecting sporadic rules.
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Krasniuk, Maxim, and Svitlana Krasnyuk. "Association rules in finance management." Thesis, Primedia eLaunch & European Scientific Platform, 2021. https://er.knutd.edu.ua/handle/123456789/18974.

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Books on the topic "Associative Rules"

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Dass, Rajanish. Classification using association rules. Ahmedabad: Indian Institute of Management, 2008.

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Kazakina, Alla. Between ruler and ruled: Freedom of association in the Russian Federation. New York, N.Y., USA: Lawyers Committee for Human Rights, 1994.

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Kaninis, A. Concurrent Mining of Association Rules. Manchester: UMIST, 1997.

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Rauch, Jan. Observational Calculi and Association Rules. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-11737-4.

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Rauch, Jan. Observational Calculi and Association Rules. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Lawyers Committee for Human Rights. Between ruler and ruled: Freedom of association in the Russian Federation. New York: Lawyers Committee for Human Rights, 1994.

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Zhang, Chengqi, and Shichao Zhang, eds. Association Rule Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46027-6.

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Association of Futures Brokers and Dealers. Rules and handbook. London: Association of Futures Brokers and Dealers, 1989.

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Institute, Indian Law. Memorandum of association rules and regulations. [New Delhi?: s.n., 1991.

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Association, Securities. The Securities Association rules and supplementary information. London: SecuritiesAssociation, 1987.

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Book chapters on the topic "Associative Rules"

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Samet, Ahmed, Eric Lefèvre, and Sadok Ben Yahia. "Classification with Evidential Associative Rules." In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 25–35. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08795-5_4.

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Thabtah, Fadi. "Ranked Multi-Label Rules Associative Classifier." In Research and Development in Intelligent Systems XXIII, 87–100. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-663-6_7.

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Chen, Jian, Jian Yin, and Jin Huang. "Mining Correlated Rules for Associative Classification." In Advanced Data Mining and Applications, 130–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_16.

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Thanajiranthorn, Chartwut, and Panida Songram. "Generation of Efficient Rules for Associative Classification." In Lecture Notes in Computer Science, 109–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33709-4_10.

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Preston, Lee E., and Duane Windsor. "Regional and Associative Regimes." In The Rules of the Game in the Global Economy: Policy Regimes for International Business, 131–64. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-8016-8_6.

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Preston, Lee E., and Duane Windsor. "Regional and Associative Regimes." In The Rules of the Game in the Global Economy: Policy Regimes for International Business, 87–122. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5354-6_5.

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Zaïane, Osmar R., and Maria-Luiza Antonie. "On Pruning and Tuning Rules for Associative Classifiers." In Lecture Notes in Computer Science, 966–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553939_136.

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Pires, Michel, Nicollas Silva, Leonardo Rocha, Wagner Meira, and Renato Ferreira. "Efficient Parallel Associative Classification Based on Rules Memoization." In Lecture Notes in Computer Science, 31–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22747-0_3.

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Punjabi, Mamta, Vineet Kushwaha, and Rashmi Ranjan. "Exploring Associative Classification Technique Using Weighted Utility Association Rules for Predictive Analytics." In High Performance Architecture and Grid Computing, 169–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22577-2_24.

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Sood, Nitakshi, Leepakshi Bindra, and Osmar Zaiane. "Bi-Level Associative Classifier Using Automatic Learning on Rules." In Lecture Notes in Computer Science, 201–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59003-1_14.

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Conference papers on the topic "Associative Rules"

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Jianchao Han. "Learning Fuzzy Association Rules and Associative Classification Rules." In 2006 IEEE International Conference on Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/fuzzy.2006.1681900.

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Su, Zhitong, Wei Song, Danyang Cao, and Jinhong Li. "Discovering Informative Association Rules for Associative Classification." In 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop (KAM 2008 Workshop). IEEE, 2008. http://dx.doi.org/10.1109/kamw.2008.4810675.

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Zhang, Yan, and Xindong Wu. "Noise Modeling with Associative Corruption Rules." In Seventh IEEE International Conference on Data Mining (ICDM 2007). IEEE, 2007. http://dx.doi.org/10.1109/icdm.2007.28.

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Chih-Hung Wu, Jing-Yi Wang, and Chien-Jung Chen. "Mining condensed rules for associative classification." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359598.

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Kundu, Gourab, Md Monirul Islam, and Sirajum Munir. "ACN: An associative classifier with negative rules." In 2008 IEEE International Conference on System of Systems Engineering (SoSE). IEEE, 2008. http://dx.doi.org/10.1109/sysose.2008.4724163.

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Kundu, Gourab, Md Monirul Islam, Sirajum Munir, and Md Faizul Bari. "ACN: An Associative Classifier with Negative Rules." In 2008 IEEE 11th International Conference on Computational Science and Engineering (CSE). IEEE, 2008. http://dx.doi.org/10.1109/cse.2008.48.

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Saengthongloun, Bordin, Thanapat Kangkachit, Thanawin Rakthanmanon, and Kitsana Waiyamai. "AC-Stream: Associative classification over data streams using multiple class association rules." In 2013 10th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2013. http://dx.doi.org/10.1109/jcsse.2013.6567349.

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Gaik-Yee Chana, Fang-Fang Chuaa, and Chien-Sing Leeb. "Fuzzy association rules vs fuzzy associative patterns in defending against web service attacks." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7381997.

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Antonie, Maria-Luiza, and Osmar R. Zaïane. "An associative classifier based on positive and negative rules." In the 9th ACM SIGMOD workshop. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1008694.1008705.

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Sangsuriyun, Sawinee, Sanparith Marukatat, and Kitsana Waiyamai. "Hierarchical Multi-label Associative Classification (HMAC) using negative rules." In 2010 9th IEEE International Conference on Cognitive Informatics (ICCI). IEEE, 2010. http://dx.doi.org/10.1109/coginf.2010.5599780.

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Reports on the topic "Associative Rules"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Tadmor, Yaakov, Zachary Lippman, David Jackson, and Dani Zamir. three crops test for the ODO breeding method. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594397.bard.

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Hybrid vigor is the leading concept that rules crops breeding for almost a century. Yet, the exact mechanism that underlies heterosis is not clear. Over dominance interaction between alleles is one of the possible explanations. Our preliminary results indicated that severe developmental mutations at the heterozygous state have significant potential to improve plant performance. This led us to propose the ‘ODO breeding method’ that is based replacing a parental line of a successful hybrid with its mutated from to improve hybrid performance. Our BARD research challenged this method in three crop systems: maize, tomato and melon. In maize we could not detect any effect of mutant heterozigosity on yield or yield components when hybrids were tested however when we analyzed the effect of heterozigosity in mutant genes at B73 genetic background we could detect ODO in yield components using certain mutants. Our results indicate that the potential of utilizing the ODO Breeding Method for maize remains unclear. In tomato we overcame technical problems we faced in creating an EMS mutation library in indeterminate glasshouse tomatoes and now we have in our hands advanced material to study the putative ODO hybrids. We transferred some of the promising ODO mutations from M82 to indeterminate glasshouse tomatoes and putative ODO hybrids are ready to be evaluated this winter. In addition, we tested the effect of In melon we compared putative ‘ODO hybrids’ with their isogenic hybrids lacking the mutant allele and our results indicated a potential for the ODO breeding method to improve yield, fruit number per plant, and carotenoids content. Additional experiments are required to estimate better the expected success percentage of the ODO breeding method in melon so that it will become a recommended practice for improving hybrid performance. Based on our results we can't yet recommend the 'ODO breeding method' as a general tool to improve hybrid performance and more efforts are necessary to evaluate the percent of success of this method. The increased carotenoid content we found in association with CRTISO heterozygosity is promising and additional experiments are currently being performed to characterize this finding.
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Megersa, Kelbesa. Tax Transparency for an Effective Tax System. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/k4d.2021.070.

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This rapid review examines evidence on the transparency in the tax system and its benefits; e.g. rising revenue, strengthen citizen/state relationship, and rule of law. Improvements in tax transparency can help in strengthening public finances in developing countries that are adversely affected by COVID-19. The current context (i.e. a global pandemic, widespread economic slowdown/recessions, and declining tax revenues) engenders the urgency of improving domestic resource mobilisation (DRM) and the fight against illicit financial flows (IFFs). Even before the advent of COVID-19, developing countries’ tax systems were facing several challenges, including weak tax administrations, low taxpayer morale and “hard-to-tax” sectors. The presence of informational asymmetry (i.e. low tax transparency) between taxpayers and tax authorities generates loopholes for abuse of the tax system. It allows the hiding of wealth abroad with a limited risk of being caught. Cases of such behaviour that are exposed without proper penalty may result in a decline in the morale of citizens and a lower level of voluntary compliance with tax legislation. A number of high-profile tax leaks and scandals have undermined public confidence in the fairness of tax systems and generated a strong demand for effective counteraction and tax transparency. One of the key contributing factors to lower tax revenues in developing countries (that is linked to low tax transparency) is a high level of IFFs. These flows, including international tax evasion and the laundering of corruption proceeds, build a major obstacle to successful DRM efforts. Research has also identified an association between organisational transparency (e.g. transparency by businesses and tax authorities) and stakeholder trust (e.g. between citizens and the state). However, the evidence is mixed as to how transparency in particular influences trust and perceptions of trustworthiness.
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Lazonick, William, and Matt Hopkins. Why the CHIPS Are Down: Stock Buybacks and Subsidies in the U.S. Semiconductor Industry. Institute for New Economic Thinking Working Paper Series, September 2021. http://dx.doi.org/10.36687/inetwp165.

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The Semiconductor Industry Association (SIA) is promoting the Creating Helpful Incentives to Produce Semiconductors (CHIPS) for America Act, introduced in Congress in June 2020. An SIA press release describes the bill as “bipartisan legislation that would invest tens of billions of dollars in semiconductor manufacturing incentives and research initiatives over the next 5-10 years to strengthen and sustain American leadership in chip technology, which is essential to our country’s economy and national security.” On June 8, 2021, the Senate approved $52 billion for the CHIPS for America Act, dedicated to supporting the U.S. semiconductor industry over the next decade. As of this writing, the Act awaits approval in the House of Representatives. This paper highlights a curious paradox: Most of the SIA corporate members now lobbying for the CHIPS for America Act have squandered past support that the U.S. semiconductor industry has received from the U.S. government for decades by using their corporate cash to do buybacks to boost their own companies’ stock prices. Among the SIA corporate signatories of the letter to President Biden, the five largest stock repurchasers—Intel, IBM, Qualcomm, Texas Instruments, and Broadcom—did a combined $249 billion in buybacks over the decade 2011-2020, equal to 71 percent of their profits and almost five times the subsidies over the next decade for which the SIA is lobbying. In addition, among the members of the Semiconductors in America Coalition (SIAC), formed specifically in May 2021 to lobby Congress for the passage of the CHIPS for America Act, are Apple, Microsoft, Cisco, and Google. These firms spent a combined $633 billion on buybacks during 2011-2020. That is about 12 times the government subsidies provided under the CHIPS for America Act to support semiconductor fabrication in the United States in the upcoming decade. If the Congress wants to achieve the legislation’s stated purpose of promoting major new investments in semiconductors, it needs to deal with this paradox. It could, for example, require the SIA and SIAC to extract pledges from its member corporations that they will cease doing stock buybacks as open-market repurchases over the next ten years. Such regulation could be a first step in rescinding Securities and Exchange Commission Rule 10b-18, which has since 1982 been a major cause of extreme income inequality and loss of global industrial competitiveness in the United States.
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Viswanathan, Meera, Jennifer Cook Middleton, Alison Stuebe, Nancy Berkman, Alison N. Goulding, Skyler McLaurin-Jiang, Andrea B. Dotson, et al. Maternal, Fetal, and Child Outcomes of Mental Health Treatments in Women: A Systematic Review of Perinatal Pharmacologic Interventions. Agency for Healthcare Research and Quality (AHRQ), April 2021. http://dx.doi.org/10.23970/ahrqepccer236.

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Background. Untreated maternal mental health disorders can have devastating sequelae for the mother and child. For women who are currently or planning to become pregnant or are breastfeeding, a critical question is whether the benefits of treating psychiatric illness with pharmacologic interventions outweigh the harms for mother and child. Methods. We conducted a systematic review to assess the benefits and harms of pharmacologic interventions compared with placebo, no treatment, or other pharmacologic interventions for pregnant and postpartum women with mental health disorders. We searched four databases and other sources for evidence available from inception through June 5, 2020 and surveilled the literature through March 2, 2021; dually screened the results; and analyzed eligible studies. We included studies of pregnant, postpartum, or reproductive-age women with a new or preexisting diagnosis of a mental health disorder treated with pharmacotherapy; we excluded psychotherapy. Eligible comparators included women with the disorder but no pharmacotherapy or women who discontinued the pharmacotherapy before pregnancy. Results. A total of 164 studies (168 articles) met eligibility criteria. Brexanolone for depression onset in the third trimester or in the postpartum period probably improves depressive symptoms at 30 days (least square mean difference in the Hamilton Rating Scale for Depression, -2.6; p=0.02; N=209) when compared with placebo. Sertraline for postpartum depression may improve response (calculated relative risk [RR], 2.24; 95% confidence interval [CI], 0.95 to 5.24; N=36), remission (calculated RR, 2.51; 95% CI, 0.94 to 6.70; N=36), and depressive symptoms (p-values ranging from 0.01 to 0.05) when compared with placebo. Discontinuing use of mood stabilizers during pregnancy may increase recurrence (adjusted hazard ratio [AHR], 2.2; 95% CI, 1.2 to 4.2; N=89) and reduce time to recurrence of mood disorders (2 vs. 28 weeks, AHR, 12.1; 95% CI, 1.6 to 91; N=26) for bipolar disorder when compared with continued use. Brexanolone for depression onset in the third trimester or in the postpartum period may increase the risk of sedation or somnolence, leading to dose interruption or reduction when compared with placebo (5% vs. 0%). More than 95 percent of studies reporting on harms were observational in design and unable to fully account for confounding. These studies suggested some associations between benzodiazepine exposure before conception and ectopic pregnancy; between specific antidepressants during pregnancy and adverse maternal outcomes such as postpartum hemorrhage, preeclampsia, and spontaneous abortion, and child outcomes such as respiratory issues, low Apgar scores, persistent pulmonary hypertension of the newborn, depression in children, and autism spectrum disorder; between quetiapine or olanzapine and gestational diabetes; and between benzodiazepine and neonatal intensive care admissions. Causality cannot be inferred from these studies. We found insufficient evidence on benefits and harms from comparative effectiveness studies, with one exception: one study suggested a higher risk of overall congenital anomalies (adjusted RR [ARR], 1.85; 95% CI, 1.23 to 2.78; N=2,608) and cardiac anomalies (ARR, 2.25; 95% CI, 1.17 to 4.34; N=2,608) for lithium compared with lamotrigine during first- trimester exposure. Conclusions. Few studies have been conducted in pregnant and postpartum women on the benefits of pharmacotherapy; many studies report on harms but are of low quality. The limited evidence available is consistent with some benefit, and some studies suggested increased adverse events. However, because these studies could not rule out underlying disease severity as the cause of the association, the causal link between the exposure and adverse events is unclear. Patients and clinicians need to make an informed, collaborative decision on treatment choices.
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