Relevant bibliographies by topics / Measures of interestingness

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Measures of interestingness'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Measures of interestingness"

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Freitas, A. A. "On rule interestingness measures." Knowledge-Based Systems 12, no. 5-6 (1999): 309–15. http://dx.doi.org/10.1016/s0950-7051(99)00019-2.

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Phan, Lan Phuong, Nghia Quoc Phan, Vinh Cong Phan, Hung Huu Huynh, Hiep Xuan Huynh, and Fabrice Guillet. "Classification of objective interestingness measures." EAI Endorsed Transactions on Context-aware Systems and Applications 3, no. 10 (2016): 151678. http://dx.doi.org/10.4108/eai.12-9-2016.151678.

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Geng, Liqiang, and Howard J. Hamilton. "Interestingness measures for data mining." ACM Computing Surveys 38, no. 3 (2006): 9. http://dx.doi.org/10.1145/1132960.1132963.

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Glass, David H. "Confirmation measures of association rule interestingness." Knowledge-Based Systems 44 (May 2013): 65–77. http://dx.doi.org/10.1016/j.knosys.2013.01.021.

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Kuznetsov, S. O., and T. Makhalova. "On interestingness measures of formal concepts." Information Sciences 442-443 (May 2018): 202–19. http://dx.doi.org/10.1016/j.ins.2018.02.032.

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Shaikh, Mateen R., Paul D. McNicholas, M. Luiza Antonie, and Thomas Brendan Murphy. "Standardizing interestingness measures for association rules." Statistical Analysis and Data Mining: The ASA Data Science Journal 11, no. 6 (2018): 282–95. http://dx.doi.org/10.1002/sam.11394.

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Susmaga, Robert, and Izabela Szczęch. "Can interestingness measures be usefully visualized?" International Journal of Applied Mathematics and Computer Science 25, no. 2 (2015): 323–36. http://dx.doi.org/10.1515/amcs-2015-0025.

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Abstract The paper presents visualization techniques for interestingness measures. The process of measure visualization provides useful insights into different domain areas of the visualized measures and thus effectively assists their comprehension and selection for different knowledge discovery tasks. Assuming a common domain form of the visualized measures, a set of contingency tables, which consists of all possible tables having the same total number of observations, is constructed. These originally four-dimensional data may be effectively represented in three dimensions using a tetrahedron
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Hao, Zhi Feng, Rui Chu Cai, Tang Wu, and Yi Yuan Zhou. "A Kernel Density Estimation Based Interestingness Measure for Association Rule Mining." Applied Mechanics and Materials 20-23 (January 2010): 389–94. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.389.

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Association rules provide a concise statement of potentially useful information, and have been widely used in real applications. However, the usefulness of association rules highly depends on the interestingness measure which is used to select interesting rules from millions of candidates. In this study, a probability analysis of association rules is conducted, and a discrete kernel density estimation based interestingness measure is proposed accordingly. The new proposed interestingness measure makes the most of the information contained in the data set and obtains much lower falsely discover
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Yang, Junrui, and Lin Xu. "A novel interestingness measure based on fusion model for association rules mining." MATEC Web of Conferences 336 (2021): 05009. http://dx.doi.org/10.1051/matecconf/202133605009.

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Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rul
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HUANG, XIANGJI. "COMPARISON OF INTERESTINGNESS MEASURES FOR WEB USAGE MINING: AN EMPIRICAL STUDY." International Journal of Information Technology & Decision Making 06, no. 01 (2007): 15–41. http://dx.doi.org/10.1142/s0219622007002368.

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A common problem in mining association rules or sequential patterns is that a large number of rules or patterns can be generated from a database, making it impossible for a human analyst to digest the results. Solutions to the problem include, among others, using interestingness measures to identify interesting rules or patterns and pruning rules that are considered redundant. Various interestingness measures have been proposed, but little work has been reported on the effectiveness of the measures on real-world applications. We present an application of Web usage mining to a large collection
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Dissertations / Theses on the topic "Measures of interestingness"

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Aldas, Cem Nuri. "An Analysis Of Peculiarity Oriented Interestingness Measures On Medical Data." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609856/index.pdf.

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Peculiar data are regarded as patterns which are significantly distinguishable from other records, relatively few in number and they are accepted as to be one of the most striking aspects of the interestingness concept. In clinical domain, peculiar records are probably signals for malignancy or disorder to be intervened immediately. The investigation of the rules and mechanisms which lie behind these records will be a meaningful contribution for improved clinical decision support systems. In order to discover the most interesting records and patterns, many peculiarity oriented interestingness
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Aljandal, Waleed A. "Itemset size-sensitive interestingness measures for association rule mining and link prediction." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1119.

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Hilderman, Robert J. "Mining summaries from databases using domain generalization graphs and objective measures of interestingness." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0010/NQ54672.pdf.

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Hahsler, Michael, and Kurt Hornik. "New Probabilistic Interest Measures for Association Rules." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/1286/1/document.pdf.

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Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start with presenting a simple probabilistic framework for transaction data which can be used to simulate transaction data when no associations are present. We use such data and a real-world database from a grocery outlet to explore the behavior of confidence and lift, two popular inte
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Huynh, Xuan-Hiep. "Interestingness Measures for Association Rules in a KDD Process : PostProcessing of Rules with ARQAT Tool." Phd thesis, Université de Nantes, 2006. http://tel.archives-ouvertes.fr/tel-00482649.

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This work takes place in the framework of Knowledge Discovery in Databases (KDD), often called "Data Mining". This domain is both a main research topic and an application ¯eld in companies. KDD aims at discovering previously unknown and useful knowledge in large databases. In the last decade many researches have been published about association rules, which are frequently used in data mining. Association rules, which are implicative tendencies in data, have the advantage to be an unsupervised model. But, in counter part, they often deliver a large number of rules. As a consequence, a postproce
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Huynh, Hiep Xuan. "Interestingness measures for association rules in a KDD process : postprocessing of rules with ARQAT tool." Nantes, 2006. http://www.theses.fr/2006NANT2110.

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Ce travail s'insère dans le cadre de l'extraction de connaissances dans les données (ECD), souvent dénommé "fouille de données". Ce domaine de recherche multidisciplinaire offre également de nombreuses applications en entreprises. L'ECD s'attache à la découverte de connaissances cachées au sein de grandes masses de données. Parmi les modèles d'extraction de connaissances disponibles, celui des règles d'association est fréquemment utilisé. Il offre l'avantage de permettre une découverte non supervisée de tendances implicatives dans les données, mais, en retour, délivre malheureusement de grande
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Masood, Adnan. "Measuring Interestingness in Outliers with Explanation Facility using Belief Networks." NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/232.

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This research explores the potential of improving the explainability of outliers using Bayesian Belief Networks as background knowledge. Outliers are deviations from the usual trends of data. Mining outliers may help discover potential anomalies and fraudulent activities. Meaningful outliers can be retrieved and analyzed by using domain knowledge. Domain knowledge (or background knowledge) is represented using probabilistic graphical models such as Bayesian belief networks. Bayesian networks are graph-based representation used to model and encode mutual relationships between entities. Due to t
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Bouker, Slim. "Contribution à l'extraction des règles d'association basée sur des préférences." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22585/document.

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Kejkula, Martin. "Zpracování asociačních pravidel metodou vícekriteriálního shlukování." Doctoral thesis, Vysoká škola ekonomická v Praze, 2002. http://www.nusl.cz/ntk/nusl-77103.

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Association rules mining is one of several ways of knowledge discovery in databases. Paradoxically, data mining itself can produce such great amounts of association rules that there is a new knowledge management problem: there can easily be thousands or even more association rules holding in a data set. The goal of this work is to design a new method for association rules post-processing. The method should be software and domain independent. The output of the new method should be structured description of the whole set of discovered association rules. The output should help user to work with d
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Desmier, Elise. "Co-evolution pattern mining in dynamic attributed graphs." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0071/document.

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Cette thèse s'est déroulée dans le cadre du projet ANR FOSTER, "FOuille de données Spatio-Temporelles : application à la compréhension et à la surveillance de l'ERosion" (ANR-2010-COSI-012-02, 2011-2014). Dans ce contexte, nous nous sommes intéressés à la modélisation de données spatio-temporelles dans des graphes enrichis de sorte que des calculs de motifs sur de telles données permettent de formuler des hypothèses intéressantes sur les phénomènes à comprendre. Plus précisément, nous travaillons sur la fouille de motifs dans des graphes relationnels (chaque noeud est identifié de fa\c con uni
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Book chapters on the topic "Measures of interestingness"

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Shekhar, Shashi, and Hui Xiong. "Interestingness Measures." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_646.

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Hilderman, Robert J., and Howard J. Hamilton. "Heuristic Measures of Interestingness." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-540-48247-5_25.

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Freitas, Alex A. "On Rule Interestingness Measures." In Research and Development in Expert Systems XV. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0835-1_10.

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Hilderman, Robert J., and Howard J. Hamilton. "Heuristic Measures of Interestingness." In Knowledge Discovery and Measures of Interest. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3283-2_4.

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Hilderman, Robert J., and Howard J. Hamilton. "An Interestingness Framework." In Knowledge Discovery and Measures of Interest. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3283-2_5.

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Salmenkivi, Marko. "Co-location Patterns, Interestingness Measures." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_153.

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Tsay, Li-Shiang. "Interestingness Measures for Actionable Patterns." In Rough Sets and Intelligent Systems Paradigms. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08729-0_27.

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Vaillant, Benoît, Philippe Lenca, and Stéphane Lallich. "A Clustering of Interestingness Measures." In Discovery Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30214-8_23.

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Salmenkivi, Marko. "Co-location Patterns, Interestingness Measures." In Encyclopedia of GIS. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_153-2.

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Salmenkivi, Marko. "Co-location Patterns, Interestingness Measures." In Encyclopedia of GIS. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_153.

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Conference papers on the topic "Measures of interestingness"

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Da Silva, Matheus Freitas, and Veronica Oliveira De Carvalho. "Aggregating Interestingness Measures in Associative Classifiers." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4405.

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A classificação associativa, a qual vem sendo muito utilizada em diversos domínios, visa a obtenção de um modelo preditivo em que o processo é baseado na extração de regras de associação. A geração do modelo ocorre em etapas, sendo uma delas voltadas a ordenar e podar um conjunto de regras. No que se refere a ordenação, uma das soluções é ranquear as regras por meio de medidas objetivas (MOs). O critério de ordenação impacta a acurácia do classificador. Nos trabalhos da literatura as MOs são exploradas individualmente. Diante do exposto, este trabalho tem por objetivo exp
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Selvarangam, K., and K. Ramesh Kumar. "Interestingness of measures: A statistical prospective." In 2014 International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2014. http://dx.doi.org/10.1109/ic3i.2014.7019800.

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"CLUSTERING INTERESTINGNESS MEASURES WITH POSITIVE CORRELATION." In 7th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002508502480253.

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Skillicorn, D. B. "Applying interestingness measures to Ansar forum texts." In ACM SIGKDD Workshop. ACM Press, 2010. http://dx.doi.org/10.1145/1938606.1938613.

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"DISCOVERING THE STABLE CLUSTERS BETWEEN INTERESTINGNESS MEASURES." In 8th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0002493701960201.

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Sengstock, Christian, Michael Gertz, and Tran Van Canh. "Spatial Interestingness Measures for Co-location Pattern Mining." In 2012 IEEE 12th International Conference on Data Mining Workshops. IEEE, 2012. http://dx.doi.org/10.1109/icdmw.2012.116.

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Zhang, Yuejin, Lingling Zhang, Guangli Nie, and Yong Shi. "A Survey of Interestingness Measures for Association Rules." In 2009 International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2009. http://dx.doi.org/10.1109/bife.2009.110.

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Reehuis, Edgar, Markus Olhofer, Michael Emmerich, Bernhard Sendhoff, and Thomas Bäck. "Novelty and interestingness measures for design-space exploration." In Proceeding of the fifteenth annual conference. ACM Press, 2013. http://dx.doi.org/10.1145/2463372.2463557.

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Yu Lan, D. Janssens, Guoqing Chen, and G. Wets. "Improving associative classification by incorporating novel interestingness measures." In IEEE International Conference on e-Business Engineering (ICEBE'05). IEEE, 2005. http://dx.doi.org/10.1109/icebe.2005.76.

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Jalali-Heravi, Mojdeh, and Osmar R. Zaïane. "A study on interestingness measures for associative classifiers." In the 2010 ACM Symposium. ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774306.

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