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Journal articles on the topic 'Minimum Utility'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Elahe, Md Fazla, and Kun Zhang. "Mining High-Utility Itemsets Based on Multiple Minimum Support and Multiple Minimum Utility Thresholds." International Journal of Database Theory and Application 10, no. 3 (2017): 31–44. http://dx.doi.org/10.14257/ijdta.2017.10.3.03.

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2

Xu, Tiantian, Jianliang Xu, and Xiangjun Dong. "Mining High Utility Sequential Patterns Using Multiple Minimum Utility." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 10 (2018): 1859017. http://dx.doi.org/10.1142/s0218001418590176.

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High utility sequential patterns (HUSP) mining has recently received a lot of attention from researchers. Many algorithms have been proposed to mine HUSP and most of them only use a single minimum utility, which implicitly assumes that all items in the database are of the same importance (such as profit), or other information based on users’ concern in the database. This is often not the case in real-life applications. Although a few methods have been proposed to mine high utility itemsets (HUI) with multiple minimum utility (MMU), they are not suitable for mining HUSP with MMU because an item

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3

Lin, Jerry Chun-Wei, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong, and Justin Zhan. "Efficient mining of high-utility itemsets using multiple minimum utility thresholds." Knowledge-Based Systems 113 (December 2016): 100–115. http://dx.doi.org/10.1016/j.knosys.2016.09.013.

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4

Krishnamoorthy, Srikumar. "Efficient mining of high utility itemsets with multiple minimum utility thresholds." Engineering Applications of Artificial Intelligence 69 (March 2018): 112–26. http://dx.doi.org/10.1016/j.engappai.2017.12.012.

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5

Challoumis, Constantinos. "Minimum Мixed Savings on Cycle of Money". Open Journal for Research in Economics 6, № 2 (2024): 61–68. http://dx.doi.org/10.32591/coas.ojre.0602.03061c.

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This paper is about the utility of the cycle of money with minimum mixed savings. This means that it examines the crucial points of tax policy and public policy that are the best for the increase of consumption and investments, subject to the case that there exists mixed savings at the minimum level. Therefore, an analysis is based on the utility of the public sector and the utility of the uncontrolled enterprises. Thence, it is plausible to extract conclusions about the utility of the cycle of money, showing the points and the behaviors of any economy when there are mixed savings and omitted

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6

Bhope, Harshal, Yash Mahajan, Swapnil Deore, and Vimla Jethani. "Association Mining for Super Market Sales using UP Growth and Top-K Algorithm." ITM Web of Conferences 32 (2020): 03012. http://dx.doi.org/10.1051/itmconf/20203203012.

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Frequent itemsets(HUIs) mining is an evolving field in data mining, that centers around finding itemsets having a utility that meets a user-specified minimum utility by finding all the itemsets. A problem arises in setting up minimum utility exactly which causes difficulties for users. By setting minimum utility underneath average, too many incessant itemsets will be generated, which in turn will make the mining process quite inefficient. No frequent itemsets will be found if the minimum utility is set too huge. The research focuses on generating frequent itemsets by using the transaction weig

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7

SIEGMANN, ARJEN. "Minimum funding ratios for defined-benefit pension funds." Journal of Pension Economics and Finance 10, no. 3 (2010): 417–34. http://dx.doi.org/10.1017/s1474747210000296.

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AbstractWe compute minimum nominal funding ratios for defined-benefit (DB) plans based on the expected utility that can be achieved in a defined-contribution (DC) pension scheme. Using Monte Carlo simulation, expected utility is computed for three different specifications of utility: power utility, mean-shortfall, and mean-downside deviation. Depending on risk aversion and the level of sophistication assumed for the DC scheme, minimum acceptable funding ratios are between 0.87 and 1.20 in nominal terms. For relative risk aversion of 5 and a DC scheme with a fixed-contribution setup, the minimu

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8

Ryang, Heungmo, Unil Yun, and Keun Ho Ryu. "Discovering high utility itemsets with multiple minimum supports." Intelligent Data Analysis 18, no. 6 (2014): 1027–47. http://dx.doi.org/10.3233/ida-140683.

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9

Aranake, A., G. A. Mashour, and M. S. Avidan. "Minimum alveolar concentration: ongoing relevance and clinical utility." Anaesthesia 68, no. 5 (2013): 512–22. http://dx.doi.org/10.1111/anae.12168.

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10

Gunawan, Ridowati, Edi Winarko, and Reza Pulungan. "A BPSO-based method for high-utility itemset mining without minimum utility threshold." Knowledge-Based Systems 190 (February 2020): 105164. http://dx.doi.org/10.1016/j.knosys.2019.105164.

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11

Sethi, Krishan Kumar, and Dharavath Ramesh. "High average-utility itemset mining with multiple minimum utility threshold: A generalized approach." Engineering Applications of Artificial Intelligence 96 (November 2020): 103933. http://dx.doi.org/10.1016/j.engappai.2020.103933.

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12

Oguz, Damla. "Ignoring Internal Utilities in High-Utility Itemset Mining." Symmetry 14, no. 11 (2022): 2339. http://dx.doi.org/10.3390/sym14112339.

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High-utility itemset mining discovers a set of items that are sold together and have utility values higher than a given minimum utility threshold. The utilities of these itemsets are calculated by considering their internal and external utility values, which correspond, respectively, to the quantity sold of each item in each transaction and profit units. Therefore, internal and external utilities have symmetric effects on deciding whether an itemset is high-utility. The symmetric contributions of both utilities cause two major related challenges. First, itemsets with low external utility value

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13

Xu, Xinping, Bo Li, Minming Li, and Lingjie Duan. "Two-facility Location Games with Minimum Distance Requirement." Journal of Artificial Intelligence Research 70 (February 17, 2021): 719–56. http://dx.doi.org/10.1613/jair.1.12319.

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We study the mechanism design problem of a social planner for locating two facilities on a line interval [0, 1], where a set of n strategic agents report their locations and a mechanism determines the locations of the two facilities. We consider the requirement of a minimum distance 0 ≤ d ≤ 1 between the two facilities. Given the two facilities are heterogeneous, we model the cost/utility of an agent as the sum of his distances to both facilities. In the heterogeneous two-facility location game to minimize the social cost, we show that the optimal solution can be computed in polynomial time an

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14

Dai, Zexing, Ying Ji, Huijie Zhang, and Panhong Cheng. "Robust Minimum Cost Consensus Model for Multicriteria Decision-Making under Uncertain Circumstances." Discrete Dynamics in Nature and Society 2022 (January 19, 2022): 1–14. http://dx.doi.org/10.1155/2022/2904761.

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Recently, the consensus model of group decision-making in uncertain circumstances has received extensive attention. Existing models focus on either minimum cost (maintain the total budget) or maximum utility (improve satisfaction). Based on the minimum cost consensus model, a new multicriteria robust minimum cost consensus model with utility preference is proposed in this paper. Firstly, considering the inherent uncertainty of input data, the unit adjustment cost of experts is described under three robust scenarios. Subsequently, a cost consensus model that expresses the views of decision-make

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15

Vasumathi, R., and S. Murugan. "Mining of High Average-Utility Pattern Using Multiple Minimum Thresholds in Big Data." Asian Journal of Computer Science and Technology 8, S2 (2019): 57–60. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2024.

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In the past years most of the research have been conducted on high average-utility itemset mining (HAUIM) with wide applications. However, most of the methods are used for centralized databases with a single machine performing the mining job. Existing algorithms cannot be applied for big data. We try to solve this issue, by developing a new method for mining high average-utility itemset mining in big data. Map Reduce also used in this paper. Many algorithms were proposed only mine HAUIs using a single minimum high average-utility threshold. In this paper we also try solve this by mining HAUIs

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16

Agrawal, Neha, and Amit Sariya. "Discovering high average utility itemsets with multiple minimum supports." International Journal of Computer Sciences and Engineering 6, no. 4 (2018): 396–99. http://dx.doi.org/10.26438/ijcse/v6i4.396399.

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17

Seo, Jun-Bae, and Hu Jin. "Minimum Cost Hypothesis Testing for Utility-Based Sleep-Mode." IEEE Communications Letters 22, no. 11 (2018): 2262–65. http://dx.doi.org/10.1109/lcomm.2018.2868353.

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18

Wilson, Stevan, and Vasilios Manousiouthakis. "Minimum utility cost for a multicomponent mass exchange operation." Chemical Engineering Science 53, no. 22 (1998): 3887–96. http://dx.doi.org/10.1016/s0009-2509(98)00216-4.

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19

Bayraktar, Erhan, and Virginia R. Young. "Correspondence between lifetime minimum wealth and utility of consumption." Finance and Stochastics 11, no. 2 (2007): 213–36. http://dx.doi.org/10.1007/s00780-007-0035-7.

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20

de Palma, André, and Karim Kilani. "Identities for maximum, minimum, and maxmin random utility models." Economics Letters 155 (June 2017): 135–39. http://dx.doi.org/10.1016/j.econlet.2017.03.018.

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21

Aabhas, Solanki*1 7. Prof. Amit Kumar Sariya2. "A CRITICAL REVIEW OF VARIOUS METHODOLOGIES FOR MINING HIGH UTILITY ITEM SETS FROM A UTILITY DATA SET." A CRITICAL REVIEW OF VARIOUS METHODOLOGIES FOR MINING HIGH UTILITY ITEM SETS FROM A UTILITY DATA SET 7, no. 6 (2020): 54–56. https://doi.org/10.5281/zenodo.3929910.

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Data Mining, also called knowledge Discovery in Database, is one of the latest research area, which has emerged in response to the Tsunami data or the flood of data, world is facing nowadays. It has taken up the challenge to develop techniques that can help humans to discover useful patterns in massive data. One such important technique is utility mining. Frequent item set mining works to discover item set which are frequently appear in transaction database, which can be discover on the basis of support and confidence value of different item set. Using frequent item set mining concept as a bas

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22

Aabhas, Solanki*1 &. Prof. Amit Kumar Sariya2. "A CRITICAL REVIEW OF VARIOUS METHODOLOGIES FOR MINING HIGH UTILITY ITEM SETS FROM A UTILITY DATA SET." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 7, no. 7 (2020): 1–3. https://doi.org/10.5281/zenodo.3931015.

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Data Mining, also called knowledge Discovery in Database, is one of the latest research area, which has emerged in response to the Tsunami data or the flood of data, world is facing nowadays. It has taken up the challenge to develop techniques that can help humans to discover useful patterns in massive data. One such important technique is utility mining. Frequent item set mining works to discover item set which are frequently appear in transaction database, which can be discover on the basis of support and confidence value of different item set. Using frequent item set mining concept as a bas

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23

Li, Hao, and Gui Lian Liu. "The Hydrogen Network Optimization of a Complex Refinery." Advanced Materials Research 396-398 (November 2011): 963–67. http://dx.doi.org/10.4028/www.scientific.net/amr.396-398.963.

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In this paper, the hydrogen distribution network of a complex refinery is optimized by the hydrogen surplus pinch method. By plotting the hydrogen purity profiles and hydrogen surplus diagrams, the hydrogen pinch with hydrogen purity of 87% is identified. The corresponding minimum hydrogen utility consumption is 17730.5 Nm3/h. Moreover, the optimal hydrogen distribution network with minimum utility consumption is designed. Compared with the current hydrogen network, the hydrogen utility consumption can be reduced by 10.45%.

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24

B. Naik, Shankar, and Jyoti D. Pawar. "Framework for High Utility Pattern Mining using Dynamically Generated Minimum Support ThresholdFramework for High Utility Pattern Mining using Dynamically Generated Minimum Support Threshold." International Journal of Engineering & Technology 7, no. 4.19 (2018): 1007. http://dx.doi.org/10.14419/ijet.v7i4.19.28276.

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In this paper we have proposed a framework which uses high utility itemset mining to store data stream elements in a compressed form and then detect events from the sliding window. This approach promises to reduce the memory requirements when applied to frequent pattern mining in data streams.In addition to this, a method to dynamically define the value of minimum support threshold based on data in the data stream is presented.

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25

Tin, Truong Chi, Tran Ngoc Anh, Duong Van Hai, and Le Hoai Bac. "HUPSMT: AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY-PROBABILITY SEQUENCES IN UNCERTAIN DATABASES WITH MULTIPLE MINIMUM UTILITY THRESHOLDS." Journal of Computer Science and Cybernetics 35, no. 1 (2019): 1–20. http://dx.doi.org/10.15625/1813-9663/35/1/13234.

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The problem of high utility sequence mining (HUSM) in quantitative se-quence databases (QSDBs) is more general than that of frequent sequence mining in se-quence databases. An important limitation of HUSM is that a user-predened minimum tility threshold is used commonly to decide if a sequence is high utility. However, this is not convincing in many real-life applications as sequences may have diferent importance. Another limitation of HUSM is that data in QSDBs are assumed to be precise. But in the real world, collected data such as by sensor maybe uncertain. Thus, this paper proposes a frame

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26

Ramana, K. Venkata, A. Muralidhar, Bhanu Chander Balusa, M. Bhavsingh, and Sravanya Majeti. "An Approach for Mining Top-k High Utility Item Sets (HUI)." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2s (2023): 198–203. http://dx.doi.org/10.17762/ijritcc.v11i2s.6045.

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Itemsets have been extracted by utilising high utility item (HUI) mining, which provides more benefits to the consumer. This could be one of the significant domains in data mining and be resourceful for several real-time implementations. Even though modern HUI mining algorithms may identify item sets that meet the minimum utility threshold, However, fixing the minimum threshold utility value has not been a simple task, and often it is intricate for the consumers when we keep the minimum utility value low. It might generate a massive amount of itemsets, and when the value is at its maximum, it

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27

Huang, Wei-Ming, Tzung-Pei Hong, Ming-Chao Chiang, and Jerry Chun-Wei Lin. "Using Multi-Conditional Minimum Thresholds in Temporal Fuzzy Utility Mining." International Journal of Computational Intelligence Systems 12, no. 2 (2019): 613. http://dx.doi.org/10.2991/ijcis.d.190426.001.

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28

Castañeda-Méndez, K. "Resolution and medical utility frequency improves on minimum distinguishable difference." Clinical Chemistry 39, no. 7 (1993): 1549–52. http://dx.doi.org/10.1093/clinchem/39.7.1549.

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29

Holiastos, Konstantinos, and Vasilios Manousiouthakis. "Minimum hot/cold/electric utility cost for heat exchange networks." Computers & Chemical Engineering 26, no. 1 (2002): 3–16. http://dx.doi.org/10.1016/s0098-1354(01)00726-8.

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30

Lambert, R. J. W., and R. Lambert. "Population distributions of minimum inhibitory concentration - increasing accuracy and utility." Journal of Applied Microbiology 100, no. 5 (2006): 999–1010. http://dx.doi.org/10.1111/j.1365-2672.2006.02842.x.

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31

Lin, Jerry Chun-Wei, Shifeng Ren, and Philippe Fournier-Viger. "MEMU: More Efficient Algorithm to Mine High Average-Utility Patterns With Multiple Minimum Average-Utility Thresholds." IEEE Access 6 (2018): 7593–609. http://dx.doi.org/10.1109/access.2018.2801261.

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32

Milan, N. Gohel. "An Examination of High Utility Item Set Mining using Different Techniques." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 479–81. https://doi.org/10.5281/zenodo.7948145.

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Finding high utility itemset from transaction databases refers to finding itemset that are profitable and useful. Frequent Itemset mining, which identifies often occurring itemsets, is expanded upon in Itemset Utility Mining. Recognising itemsets with utility values over a certain ven utility threshold is the aim of high utility itemset mining. The user-specified minimum support threshold value must be met for an itemset to be considered a high utility itemset; otherwise, it is treated as a low utility itemset. In this article, we give a literature review of the current state of research, as w

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33

Foo, Dominic Chwan Yee, and Zainuddin Abdul Manan. "Setting the Minimum Utility Gas Flowrate Targets Using Cascade Analysis Technique." Industrial & Engineering Chemistry Research 45, no. 17 (2006): 5986–95. http://dx.doi.org/10.1021/ie051322k.

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34

Mathew, Shadrack Uzoma, and Tamzor Lebari Aban. "Design and Cost Optimization of Heat Exchangers Network System in a Typical Brewery Plant." European Journal of Engineering Research and Science 5, no. 3 (2020): 245–51. http://dx.doi.org/10.24018/ejers.2020.5.3.1748.

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Heat exchanger design and cost optimization had been carried out for Pabod Brewery, Port Harcourt, Rivers State, Nigeria using Pinch Technology as process application method. The gross energy expenditure by the plant is 10.44MW at production capacity of 400,000 liters of beer per day. On quantitative aggregate 6.157MW goes for heating and 4.267MW for cooling. A temperature pinch or minimum approach temperature (ΔTmin) of 100C, minimum heating utility of 5.04MW and cooling utility of 3.09MW were recorded. Energy upturn of 1.08MW and 1.23MW for the hot and cold flows were measured. This finding

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35

Mathew, Shadrack Uzoma, and Tamzor Lebari Aban. "Design and Cost Optimization of Heat Exchangers Network System in a Typical Brewery Plant." European Journal of Engineering and Technology Research 5, no. 3 (2020): 245–51. http://dx.doi.org/10.24018/ejeng.2020.5.3.1748.

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Heat exchanger design and cost optimization had been carried out for Pabod Brewery, Port Harcourt, Rivers State, Nigeria using Pinch Technology as process application method. The gross energy expenditure by the plant is 10.44MW at production capacity of 400,000 liters of beer per day. On quantitative aggregate 6.157MW goes for heating and 4.267MW for cooling. A temperature pinch or minimum approach temperature (?Tmin) of 100C, minimum heating utility of 5.04MW and cooling utility of 3.09MW were recorded. Energy upturn of 1.08MW and 1.23MW for the hot and cold flows were measured. This finding

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36

Segal, Tom, Asaf Shabtai, and Yuval Elovici. "DOMBA: Double Model Balancing for Access-Controlled Language Models via Minimum-Bounded Aggregation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 25101–9. https://doi.org/10.1609/aaai.v39i23.34695.

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The utility of large language models (LLMs) depends heavily on the quality and quantity of their training data. Many organizations possess large data corpora that could be leveraged to train or fine-tune LLMs tailored to their specific needs. However, these datasets often come with access restrictions that are based on user privileges and enforced by access control mechanisms. Training LLMs on such datasets could result in exposure of sensitive information to unauthorized users. A straightforward approach for preventing such exposure is to train a separate model for each access level. This, ho

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37

Meruva, Subba Reddy, and Venkateswarlu Bondu. "Review of Association Mining Methods for the Extraction of Rules Based on the Frequency and Utility Factors." International Journal of Information Technology Project Management 12, no. 4 (2021): 1–10. http://dx.doi.org/10.4018/ijitpm.2021100101.

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Association rule defines the relationship among the items and discovers the frequent items using a support-confidence framework. This framework establishes user-interested or strong association rules with two thresholds (i.e., minimum support and minimum confidence). Traditional association rule mining methods (i.e., apriori and frequent pattern growth [FP-growth]) are widely used for discovering of frequent itemsets, and limitation of these methods is that they are not considering the key factors of the items such as profit, quantity, or cost of items during the mining process. Applications l

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38

Homburg, Carsten, André Hoppe, Roman Schick, and Amelie Braul. "Accounting for preference dependency in target costing – a note." Review of Quantitative Finance and Accounting 57, no. 3 (2021): 845–58. http://dx.doi.org/10.1007/s11156-021-00962-9.

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AbstractTarget costing is a well-established strategic cost management tool in theory and practice. The original target costing model implies independence of customer preferences resulting in additive utility functions for the customer-oriented optimization of cost structures. We argue that this independence of preferences is not given until a minimum variant of a product is reached that provides its inherent functionality. This is reasonable since one cannot assign customer utility to a product that does not function in its most basic way. Our modified model accounts for the dependency of cus

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39

Reichman, Jerome H. "Compliance of Canada’s Utility Doctrine with International Minimum Standards of Patent Protection." Proceedings of the ASIL Annual Meeting 108 (2014): 313–17. http://dx.doi.org/10.5305/procannmeetasil.108.0313.

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40

McMunigal, Kevin C. "Desert, Utility, and Minimum Contacts: Toward a Mixed Theory of Personal Jurisdiction." Yale Law Journal 108, no. 1 (1998): 189. http://dx.doi.org/10.2307/797473.

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41

Mann, Stefan. "Individual Utility Effects of Minimum Wages in a New Activity-Choice Framework." Forum for Social Economics 49, no. 1 (2014): 99–108. http://dx.doi.org/10.1080/07360932.2014.946944.

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42

Monisha, D., and Kumar Arul. "Mining High Utility Dataset." International Journal of Trend in Scientific Research and Development 2, no. 3 (2019): 2136–43. https://doi.org/10.31142/ijtsrd11691.

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High Utility Dataset mining is a popular tactics in the data mining, which bond to search all datasets having a profit higher than a customer specified minimum profit point. Although, setting appropriate value is a trouble for the customers. If the point is set to be too low, too many HUDs will be catalyzed, which may result in the mining process very ineffectual. And also, if the point is set to be too high, it results with no Products will be found. Setting value is a problem by proposing a new configuration for high utility dataset mining, where k is the desired number of Products to be min

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Коновальчук, Evgeniy Konovalchuk, Коновалов, Oleg Konovalov, Сербулов, and Yuriy Serbulov. "The problem solution of the uniform distribution of resources by the dynamic programming method." Forestry Engineering Journal 6, no. 3 (2016): 248–54. http://dx.doi.org/10.12737/21706.

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The solution of two problems is considered for this purpose in the article: the achievements of a conditional minimum and the achievement of an absolute minimum according to the devel-oped algorithms. At the same time the problem solution of achieving the conditional minimum is reduced to theproblem of integer quadratic programming and the problem of achieving the abso-lute minimum – to minimization of the utility function in the field of the set restrictions and to the search of the fixed number of variables.

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Violante, Michael, Hassan Davani, and Saeed D. Manshadi. "A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters." Water 14, no. 16 (2022): 2483. http://dx.doi.org/10.3390/w14162483.

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In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probability of utility pole failure through conventional hydrological, hydrostatic, and geotechnical calculations embedded to a unique mixed integer linear programming (MILP) optimization framework. Once the flow rates that cause utility pole overturn are determined, the most cost-efficient subterranean pipe network configuration can be created that will allow for flood waters to be redire

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Williams, H. R., R. S. Trask, P. M. Weaver, and I. P. Bond. "Minimum mass vascular networks in multifunctional materials." Journal of The Royal Society Interface 5, no. 18 (2007): 55–65. http://dx.doi.org/10.1098/rsif.2007.1022.

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A biomimetic analysis is presented in which an expression for the optimum vessel diameter for the design of minimum mass branching or vascular networks in engineering applications is derived. Agreement with constructal theory is shown. A simple design case is illustrated and application to more complex cases with branching networks of several generations discussed. The analysis is also extended into the turbulent flow regime, giving an optimization tool with considerable utility in the design of fluid distribution systems. The distribution of vessel lengths in different generations was also fo

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46

Holiastos, Konstantinos, and Vasilios Manousiouthakis. "Minimum hot-cold and electric utility cost for a finite-capacity reservoir system." Computers & Chemical Engineering 23, no. 9 (1999): 1263–76. http://dx.doi.org/10.1016/s0098-1354(99)00288-4.

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47

Drake, James E., and Vasilios Manousiouthakis. "IDEAS approach to process network synthesis: minimum utility cost for complex distillation networks." Chemical Engineering Science 57, no. 15 (2002): 3095–106. http://dx.doi.org/10.1016/s0009-2509(02)00159-8.

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48

Brett, Craig, and John A. Weymark. "Voting over selfishly optimal nonlinear income tax schedules with a minimum-utility constraint." Journal of Mathematical Economics 67 (December 2016): 18–31. http://dx.doi.org/10.1016/j.jmateco.2016.09.001.

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49

Mathew, Shadrack Uzoma, and Lebari Aban Tamzor. "Heat Exchanger Process Optimization in A Typical Brewery Plant." European Journal of Engineering Research and Science 5, no. 1 (2020): 76–81. http://dx.doi.org/10.24018/ejers.2020.5.1.1598.

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The research attempts to improve upon the performance efficiency of the heat exchanger network system of Pabod Brewery, Port Harcourt, Rivers State, Nigeria. It swaps the heat system of the plant by the use of Pinch Technology to recover waste heat and integrating the recovered energy for process application. The application software is Microsoft Excel and Problem Table Method was employed in the numerical analysis of data. The gross energy expenditure by the plant is 10.44MW at production capacity of 400,000 liters of beer per day. On quantitative aggregate 6.157MW goes for heating and 4.267M

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50

Mathew, Shadrack Uzoma, and Lebari Aban Tamzor. "Heat Exchanger Process Optimization in A Typical Brewery Plant." European Journal of Engineering and Technology Research 5, no. 1 (2020): 76–81. http://dx.doi.org/10.24018/ejeng.2020.5.1.1598.

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Abstract:

The research attempts to improve upon the performance efficiency of the heat exchanger network system of Pabod Brewery, Port Harcourt, Rivers State, Nigeria. It swaps the heat system of the plant by the use of Pinch Technology to recover waste heat and integrating the recovered energy for process application. The application software is Microsoft Excel and Problem Table Method was employed in the numerical analysis of data. The gross energy expenditure by the plant is 10.44MW at production capacity of 400,000 liters of beer per day. On quantitative aggregate 6.157MW goes for heating and 4.267M

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