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Journal articles on the topic 'Measures of interestingness'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

da Jiménez, Aí, Fernando Berzal, and Juan-Carlos Cubero. "Interestingness measures for association rules within groups." Intelligent Data Analysis 17, no. 2 (2013): 195–215. http://dx.doi.org/10.3233/ida-130574.

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12

Baena-Garcı´a, M., and R. Morales-Bueno. "Mining interestingness measures for string pattern mining." Knowledge-Based Systems 25, no. 1 (2012): 45–50. http://dx.doi.org/10.1016/j.knosys.2011.01.013.

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13

Kontonasios, Kleanthis-Nikolaos, Eirini Spyropoulou, and Tijl De Bie. "Knowledge discovery interestingness measures based on unexpectedness." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, no. 5 (2012): 386–99. http://dx.doi.org/10.1002/widm.1063.

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14

Bouker, Slim, Rabie Saidi, Sadok Ben Yahia, and Engelbert Mephu Nguifo. "Mining Undominated Association Rules Through Interestingness Measures." International Journal on Artificial Intelligence Tools 23, no. 04 (2014): 1460011. http://dx.doi.org/10.1142/s0218213014600112.

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The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. In order to bypass this hamper, an efficient selection of rules has to be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures gave rise to a n

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15

Sudarsanam, Nandan, Nishanth Kumar, Abhishek Sharma, and Balaraman Ravindran. "Rate of change analysis for interestingness measures." Knowledge and Information Systems 62, no. 1 (2019): 239–58. http://dx.doi.org/10.1007/s10115-019-01352-3.

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16

Masood, Adnan, and Sofiane Ouaguenouni. "Probabilistic Measures for Interestingness of Deviations - A Survey." International Journal of Artificial Intelligence & Applications 4, no. 2 (2013): 1–12. http://dx.doi.org/10.5121/ijaia.2013.4201.

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17

MCGARRY, KEN. "A survey of interestingness measures for knowledge discovery." Knowledge Engineering Review 20, no. 1 (2005): 39–61. http://dx.doi.org/10.1017/s0269888905000408.

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It is a well-known fact that the data mining process can generate many hundreds and often thousands of patterns from data. The task for the data miner then becomes one of determining the most useful patterns from those that are trivial or are already well known to the organization. It is therefore necessary to filter out those patterns through the use of some measure of the patterns actual worth. This article presents a review of the available literature on the various measures devised for evaluating and ranking the discovered patterns produced by the data mining process. These so-called inter

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18

Zbidi, Naim, Sami Faiz, and Mohamed Limam. "On Mining Summaries by Objective Measures of Interestingness." Machine Learning 62, no. 3 (2006): 175–98. http://dx.doi.org/10.1007/s10994-005-5066-8.

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19

Glass, David H. "Entailment and symmetry in confirmation measures of interestingness." Information Sciences 279 (September 2014): 552–59. http://dx.doi.org/10.1016/j.ins.2014.04.010.

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20

Lan, Yu, Davy Janssens, Guoqing Chen, and Geert Wets. "Improving associative classification by incorporating novel interestingness measures." Expert Systems with Applications 31, no. 1 (2006): 184–92. http://dx.doi.org/10.1016/j.eswa.2005.09.015.

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21

Greco, Salvatore, Roman Słowiński, and Izabela Szczęch. "Properties of rule interestingness measures and alternative approaches to normalization of measures." Information Sciences 216 (December 2012): 1–16. http://dx.doi.org/10.1016/j.ins.2012.05.018.

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22

Shaharanee, Izwan Nizal Mohd, Fedja Hadzic, and Tharam S. Dillon. "Interestingness measures for association rules based on statistical validity." Knowledge-Based Systems 24, no. 3 (2011): 386–92. http://dx.doi.org/10.1016/j.knosys.2010.11.005.

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23

Asha, P., T. Prem Jacob, and A. Pravin. "Finding Efficient Positive and Negative Itemsets Using Interestingness Measures." International Journal of Engineering & Technology 7, no. 4.36 (2018): 533. http://dx.doi.org/10.14419/ijet.v7i4.36.24133.

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Currently, data gathering techniques have increased through which unstructured data creeps in, along with well defined data formats. Mining these data and bringing out useful patterns seems difficult. Various data mining algorithms were put forth for this purpose. The associated patterns generated by the association rule mining algorithms are large in number. Every ARM focuses on positive rule mining and very few literature has focussed on rare_itemsets_mining. The work aims at retrieving the rare itemsets that are of most interest to the user by utilizing various interestingness measures. Bot

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24

Greco, Salvatore, Roman Słowiński, and Izabela Szczęch. "Measures of rule interestingness in various perspectives of confirmation." Information Sciences 346-347 (June 2016): 216–35. http://dx.doi.org/10.1016/j.ins.2016.01.056.

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25

Wang, Meihua, Shumin Wu, and Ruichu Cai. "Two novel interestingness measures for gene association rule mining." Neural Computing and Applications 23, no. 3-4 (2012): 835–41. http://dx.doi.org/10.1007/s00521-012-1005-3.

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26

KARASU, Başar, and Onur DOĞAN. "ASSOCIATION RULE MINING AND INTERESTINGNESS MEASURES: A CASE STUDY." Journal of Business in The Digital Age 3, no. 2 (2020): 94–107. http://dx.doi.org/10.46238/jobda.811464.

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27

Gruner, Charles R. "A Quasiexperimental Study of the Effect of Humor Preference and other Variables on Understanding/Appreciation of Editorial Satire." Psychological Reports 65, no. 3 (1989): 967–70. http://dx.doi.org/10.2466/pr0.1989.65.3.967.

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Students indicated their humor preference (nonsense vs philosophical) and personal data on 7 other independent variables, then read three satirical editorials and, after each, checked which of five statements was the thesis intended by the author. They also rated each satire on interestingness and funniness. The number of satires' theses correctly identified was the dependent variable (understanding) and the interestingness and funniness scales were the measures of appreciation. The only significant value of chi-squared for understanding was by Greek/nonGreek status. A correlation matrix of th

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28

Ju, Chunhua, Fuguang Bao, Chonghuan Xu, and Xiaokang Fu. "A Novel Method of Interestingness Measures for Association Rules Mining Based on Profit." Discrete Dynamics in Nature and Society 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/868634.

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Association rules mining is an important topic in the domain of data mining and knowledge discovering. Some papers have presented several interestingness measure methods; the most typical areSupport,Confidence,Lift,Improve, and so forth. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative relationship, and so forth. This paper proposes three new methods,Bi-lift, Bi-improve, andBi-confidence, forLift, Improve, and Confidence, respectively. Then, on the basis of utility function and the executing cost of rules, we propose inte

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29

Ramkumar, Thirunavukarasu, Rengaramanujam Srinivasan, and Shanmugasundaram Hariharan. "Synthesizing Global Association Rules from Different Data Sources Based on Desired Interestingness Metrics." International Journal of Information Technology & Decision Making 13, no. 03 (2014): 473–95. http://dx.doi.org/10.1142/s0219622014500138.

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Since business houses are generally global, the required data for their corporate decisions are spread over multiple branches at different regions. In such circumstances, local pattern analysis-based global pattern discovery has become an efficient strategy for mining their multiple data sources. The traditional support-confidence framework alone is not enough for assessing the interestingness of synthesized global association rules. In this context, numerous interestingness measures have been developed in the past to meet various situations. Depending on the requirement, local branches and th

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30

LI, Xianneng, Shingo MABU, Huiyu ZHOU, Kaoru SHIMADA, and Kotaro HIRASAWA. "Analysis of Various Interestingness Measures in Class Association Rule Mining." SICE Journal of Control, Measurement, and System Integration 4, no. 4 (2011): 295–304. http://dx.doi.org/10.9746/jcmsi.4.295.

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31

Christopher, Jabez J., Khanna H. Nehemiah, and Kannan Arputharaj. "Knowledge-based Systems and Interestingness Measures: Analysis with Clinical datasets." Journal of Computing and Information Technology 24, no. 1 (2016): 65–78. http://dx.doi.org/10.20532/cit.2016.1002500.

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32

Susmaga, R., and I. Szczęch. "Visualization support for the analysis of properties of interestingness measures." Bulletin of the Polish Academy of Sciences Technical Sciences 63, no. 1 (2015): 315–27. http://dx.doi.org/10.1515/bpasts-2015-0036.

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Abstract The paper considers a particular group of rule interestingness measures, called Bayesian confirmation measures, which have become the subject of numerous, but often exclusively theoretical studies. To assist and enhance their analysis in real-life situations, where time constraints may impede conducting such time consuming procedures, a visual technique has been introduced and described in this paper. It starts with an exhaustive and non-redundant set of contingency tables, which consists of all possible tables having the same number of observations. These data, originally 4-dimension

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33

Park, Hee Chang. "A study on the relatively causal strength measures in a viewpoint of interestingness measure." Journal of the Korean Data and Information Science Society 28, no. 1 (2017): 49–56. http://dx.doi.org/10.7465/jkdi.2017.28.1.49.

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34

Armand, André Totohasina, and Daniel Rajaonasy Feno. "An Extension of Totohasina’s Normalization Theory of Quality Measures of Association Rules." International Journal of Mathematics and Mathematical Sciences 2019 (January 29, 2019): 1–7. http://dx.doi.org/10.1155/2019/7829805.

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In the context of binary data mining, for unifying view on probabilistic quality measures of association rules, Totohasina’s theory of normalization of quality measures of association rules primarily based on affine homeomorphism presents some drawbacks. Indeed, it cannot normalize some interestingness measures which are explained below. This paper presents an extension of it, as a new normalization method based on proper homographic homeomorphism that appears most consequent.

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35

SOMYANONTHANAKUL, Rachasak, and Thanaruk THEERAMUNKONG. "Characterization of Interestingness Measures Using Correlation Analysis and Association Rule Mining." IEICE Transactions on Information and Systems E103.D, no. 4 (2020): 779–88. http://dx.doi.org/10.1587/transinf.2019iip0008.

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36

Selvarangam. "SELECTING PERFECT INTERESTINGNESS MEASURES BY COEFFICIENT OF VARIATION BASED RANKING ALGORITHM." Journal of Computer Science 10, no. 9 (2014): 1672–79. http://dx.doi.org/10.3844/jcssp.2014.1672.1679.

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37

Lallich, Stéphane, Benoît Vaillant, and Philippe Lenca. "A Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures." Methodology and Computing in Applied Probability 9, no. 3 (2007): 447–63. http://dx.doi.org/10.1007/s11009-007-9025-7.

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38

Benites, Fernando, and Elena Sapozhnikova. "Evaluation of Hierarchical Interestingness Measures for Mining Pairwise Generalized Association Rules." IEEE Transactions on Knowledge and Data Engineering 26, no. 12 (2014): 3012–25. http://dx.doi.org/10.1109/tkde.2014.2320722.

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39

Ramdasi, Swati R., Shailaja C. Shirwaikar, and Vilas Kharat. "Interestingness measures for quantified and ordered categorical attributes using fuzzy approach." International Journal of Fuzzy Computation and Modelling 2, no. 4 (2019): 353. http://dx.doi.org/10.1504/ijfcm.2019.10022114.

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40

Ramdasi, Swati R., Shailaja C. Shirwaikar, and Vilas Kharat. "Interestingness measures for quantified and ordered categorical attributes using fuzzy approach." International Journal of Fuzzy Computation and Modelling 2, no. 4 (2019): 353. http://dx.doi.org/10.1504/ijfcm.2019.100348.

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41

Ohsaki, Miho, Hidenao Abe, Shusaku Tsumoto, Hideto Yokoi, and Takahira Yamaguchi. "Evaluation of rule interestingness measures in medical knowledge discovery in databases." Artificial Intelligence in Medicine 41, no. 3 (2007): 177–96. http://dx.doi.org/10.1016/j.artmed.2007.07.005.

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42

Zimmermann, Albrecht. "Objectively evaluating condensed representations and interestingness measures for frequent itemset mining." Journal of Intelligent Information Systems 45, no. 3 (2013): 299–317. http://dx.doi.org/10.1007/s10844-013-0297-9.

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43

Wu, Tianyi, Yuguo Chen, and Jiawei Han. "Re-examination of interestingness measures in pattern mining: a unified framework." Data Mining and Knowledge Discovery 21, no. 3 (2010): 371–97. http://dx.doi.org/10.1007/s10618-009-0161-2.

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44

Vo, Bay, and Bac Le. "Interestingness measures for association rules: Combination between lattice and hash tables." Expert Systems with Applications 38, no. 9 (2011): 11630–40. http://dx.doi.org/10.1016/j.eswa.2011.03.042.

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45

Hussein, Nada, Abdallah Alashqur, and Bilal Sowan. "Using the interestingness measure lift to generate association rules." Journal of Advanced Computer Science & Technology 4, no. 1 (2015): 156. http://dx.doi.org/10.14419/jacst.v4i1.4398.

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<p>In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for finding frequent patterns and confidence for finding association rules. A shortcoming of confid

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46

Paul, Razan, Tudor Groza, Jane Hunter, and Andreas Zankl. "Semantic interestingness measures for discovering association rules in the skeletal dysplasia domain." Journal of Biomedical Semantics 5, no. 1 (2014): 8. http://dx.doi.org/10.1186/2041-1480-5-8.

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47

Tew, C., C. Giraud-Carrier, K. Tanner, and S. Burton. "Behavior-based clustering and analysis of interestingness measures for association rule mining." Data Mining and Knowledge Discovery 28, no. 4 (2013): 1004–45. http://dx.doi.org/10.1007/s10618-013-0326-x.

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48

Johnson, Colin G., Jon McCormack, Iria Santos, and Juan Romero. "Understanding Aesthetics and Fitness Measures in Evolutionary Art Systems." Complexity 2019 (March 20, 2019): 1–14. http://dx.doi.org/10.1155/2019/3495962.

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One of the general aims of evolutionary art research is to build a computer system capable of creating interesting, beautiful, or creative results, including images, videos, animations, text, and performances. In this context, it is crucial to understand how fitness is conceived and implemented to explore the “interestingness,” beauty, or creativity that the system is capable of. In this paper, we survey the recent research on fitness for evolutionary art related to aesthetics. We also cover research in the psychology of aesthetics, including relation between complexity and aesthetics, measure

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49

Phan, Nghia Quoc, Vinh Cong Phan, Hung Huu Huynh, and Hiep Xuan Huynh. "Clustering the objective interestingness measures based on tendency of variation in statistical implications." EAI Endorsed Transactions on Context-aware Systems and Applications 3, no. 9 (2016): 151212. http://dx.doi.org/10.4108/eai.2-5-2016.151212.

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50

Lee, Dongwon. "A Regression-Model-based Method for Combining Interestingness Measures of Association Rule Mining." Journal of Intelligence and Information Systems 23, no. 1 (2017): 127–41. http://dx.doi.org/10.13088/jiis.2017.23.1.127.

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