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Artykuły w czasopismach na temat „Utility Mining”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

D, Monisha, and Arul Kumar. "Mining High Utility Dataset." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 2136–43. http://dx.doi.org/10.31142/ijtsrd11691.

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Lin, Jerry Chun-Wei, Wensheng Gan, Philippe Fournier-Viger, et al. "High utility-itemset mining and privacy-preserving utility mining." Perspectives in Science 7 (March 2016): 74–80. http://dx.doi.org/10.1016/j.pisc.2015.11.013.

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SHILPA, GHODE. "STUDY OF HIGH UTILITY PATTERN MINING ALGORITHMS AND COMPARISION OF D2HUP AND MAHUSP ALGORITHMS." GLOBAL JOURNAL OF ADVANCED ENGINEERING TECHNOLOGIES AND SCIENCES 5, no. 3 (2018): 1–7. https://doi.org/10.5281/zenodo.1195101.

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Discovering interesting patterns and useful knowledge from massive data has become an important data mining task.  These days, we come across a lot of things that have profit technically referred as external utility, value greater than the other item sets in the database.  Utility mining is an important topic in data mining and has received extensive research in last few years. In utility mining, each item is associated with a utility that could be profit, quantity, cost or other user preferences. Objective of Utility Mining is to identify the item sets with highest utilities. High u

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Shibi, B. "Systolic Tree Algorithms for Discovering High Utility Item sets from Transactional Database." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 01 (2014): 499–502. https://doi.org/10.5281/zenodo.14620781.

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Utility mining emerges as an important topic in data mining field. Here high utility itemsets mining refers to importance or profitability of an item to users. Efficient mining of high utility itemsets plays an important role in many re al-life applications and is an important research issue in data mining area. Number of Algorithms utility pattern growth (UP-Growth) and UP-Growth+( A data structure having tree like structure named utility pattern tree (UP-Tree)) is used for storing the information about high utility item set such that by using only double scanning of database, candidate items

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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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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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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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Manges, John. "D2Cell data mining utility." ACM SIGAPL APL Quote Quad 31, no. 2 (2000): 47–54. http://dx.doi.org/10.1145/570406.570412.

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LIU, YING, JIANWEI LI, WEI-KENG LIAO, ALOK CHOUDHARY, and YONG SHI. "HIGH UTILITY ITEMSETS MINING." International Journal of Information Technology & Decision Making 09, no. 06 (2010): 905–34. http://dx.doi.org/10.1142/s0219622010004159.

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High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web service, since items are actually different in many aspects in real applications. However, due to the lack of "downward closure property", the cost of candidate generation of high utility itemsets mining is intolerable in terms of time and memory

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10

K., Rajendra Prasad. "Optimized High-Utility Itemsets Mining for Effective Association Mining Paper." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2911–18. https://doi.org/10.11591/ijece.v7i5.pp2911-2918.

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Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations dur

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Prasad, K. Rajendra. "Optimized High-Utility Itemsets Mining for Effective Association Mining Paper." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2911. http://dx.doi.org/10.11591/ijece.v7i5.pp2911-2918.

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Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations dur

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N.T, Tung, Nguyen Le Van, Trinh Cong Nhut, and Tran Van Sang. "MINING OF HIGH-UTILITY ITEMSETS WITH NEGATIVE UTILITY." JOURNAL OF TECHNOLOGY & INNOVATION 1, no. 2 (2020): 44–47. http://dx.doi.org/10.26480/jtin.02.2021.44.47.

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The goal of the high-utility itemset mining task is to discover combinations of items that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Modified Efficient High‐utility Itemsets mining with Negative utility (MEHIN) to find all HUIs with negative utility. This algorithm is an improved version of the EHIN algorithm. MEHIN utilizes 2 new upper bounds for pruning, named revised subt

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Singh, Kuldeep, Harish Kumar Shakya, Abhimanyu Singh, and Bhaskar Biswas. "Mining of high-utility itemsets with negative utility." Expert Systems 35, no. 6 (2018): e12296. http://dx.doi.org/10.1111/exsy.12296.

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Hwang, Jeong-Hee. "High Utility Itemset Mining using Utility-List Structure." Journal of Digital Contents Society 21, no. 3 (2020): 579–86. http://dx.doi.org/10.9728/dcs.2020.21.3.579.

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15

Patra, Rakesh. "Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 173–81. http://dx.doi.org/10.17762/msea.v70i1.2297.

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Data mining is the process of extracting new, possibly useful information from vast data bases that is not straightforward. Market basket analysis, a kind of data mining used in retail research, is used to analyse client transactions. The association between the things that occur in transactions more frequently was the focus of earlier data mining techniques. They don't take an item's significance or utility into account while often mining an itemset. Utility mining is a new field that has emerged as a result of the limits of common mining goods. When mining, the profitability or utility of an

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Huang, Wei-Ming, Tzung-Pei Hong, Guo-Cheng Lan, Ming-Chao Chiang, and Jerry Chun-Wei Lin. "Temporal-Based Fuzzy Utility Mining." IEEE Access 5 (2017): 26639–52. http://dx.doi.org/10.1109/access.2017.2774510.

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Chaudhari, Mr Hemant Narorottam, Mr Gajendra Singh Chandel, and Mr Kailas Patidar. "High Utility Item Set Mining." International Journal of Engineering Trends and Technology 12, no. 3 (2014): 149–51. http://dx.doi.org/10.14445/22315381/ijett-v12p228.

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Kannimuthu, S., Dr K. Premalatha, and S. Shankar. "iFUM Improved Fast Utility Mining." International Journal of Computer Applications 27, no. 11 (2011): 32–36. http://dx.doi.org/10.5120/3343-4602.

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Gan, Wensheng, Jerry Chun-Wei Lin, Han-Chieh Chao, Hamido Fujita, and Philip S. Yu. "Correlated utility-based pattern mining." Information Sciences 504 (December 2019): 470–86. http://dx.doi.org/10.1016/j.ins.2019.07.005.

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LAN, GUO-CHENG, TZUNG-PEI HONG, and VINCENT S. TSENG. "EFFICIENTLY MINING HIGH AVERAGE-UTILITY ITEMSETS WITH AN IMPROVED UPPER-BOUND STRATEGY." International Journal of Information Technology & Decision Making 11, no. 05 (2012): 1009–30. http://dx.doi.org/10.1142/s0219622012500307.

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Utility mining has recently been discussed in the field of data mining. A utility itemset considers both profits and quantities of items in transactions, and thus its utility value increases with increasing itemset length. To reveal a better utility effect, an average-utility measure, which is the total utility of an itemset divided by its itemset length, is proposed. However, existing approaches use the traditional average-utility upper-bound model to find high average-utility itemsets, and thus generate a large number of unpromising candidates in the mining process. The present study propose

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21

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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Saeed, Rashad, Azhar Rauf, Fahmi H. Quradaa, and Syed Muhammad Asim. "Efficient Utility Tree-Based Algorithm to Mine High Utility Patterns Having Strong Correlation." Complexity 2021 (July 27, 2021): 1–18. http://dx.doi.org/10.1155/2021/7310137.

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High Utility Itemset Mining (HUIM) is one of the most investigated tasks of data mining. It has broad applications in domains such as product recommendation, market basket analysis, e-learning, text mining, bioinformatics, and web click stream analysis. Insights from such pattern analysis provide numerous benefits, including cost cutting, improved competitive advantage, and increased revenue. However, HUIM methods may discover misleading patterns as they do not evaluate the correlation of extracted patterns. As a consequence, a number of algorithms have been proposed to mine correlated HUIs. T

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Rahmati, Bahareh, and Mohammad Karim Sohrabi. "A Systematic Survey on High Utility Itemset Mining." International Journal of Information Technology & Decision Making 18, no. 04 (2019): 1113–85. http://dx.doi.org/10.1142/s0219622019300027.

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High utility itemset mining considers unit profits and quantities of items in a transaction database to extract more applicable and more useful association rules. Downward closure property, which causes significant pruning in frequent itemset mining, is not established in the utility of itemsets and so the mining problem will require alternative solutions to reduce its search space and to enhance its efficiency. Using an anti-monotonic upper bound of the utility function and exploiting efficient data structures for storing and compacting the dataset to perform efficient pruning strategies are

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Bokir, Abdullah, and Vb Narasimha. "High Utility Pattern Mining: A Survey on Current and Possible Areas of Applications." Review of Information Engineering and Applications 9, no. 1 (2022): 38–49. http://dx.doi.org/10.18488/79.v9i1.3236.

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High Utility Pattern Mining (HUPM) has a wide range of applications, including making recommendations, detecting outliers, analyzing customer behaviors, and solving a wide range of other problems. In fact, unlike other important data mining tasks such as outlier analysis and classification, high utility pattern mining can be used as an intermediary tool for providing pattern-centered insights for other data mining tasks. In this paper, we look at a wide range of different applications of high utility pattern mining that are available in the literature. We gathered the literature review papers

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V.Barhate, Prashant, S. R. Chaudhari, and P. C. Gill. "Efficient High Utility Itemset Mining using Utility Information Record." International Journal of Computer Applications 120, no. 4 (2015): 34–39. http://dx.doi.org/10.5120/21219-3940.

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Song, Wei, Lu Liu, and Chaomin Huang. "Generalized maximal utility for mining high average-utility itemsets." Knowledge and Information Systems 63, no. 11 (2021): 2947–67. http://dx.doi.org/10.1007/s10115-021-01614-z.

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Lee, Serin, and Jong Soo Park. "High Utility Itemset Mining Using Transaction Utility of Itemsets." KIPS Transactions on Software and Data Engineering 4, no. 11 (2015): 499–508. http://dx.doi.org/10.3745/ktsde.2015.4.11.499.

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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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Arunkumar, M. S., P. Suresh, and C. Gunavathi. "High Utility Itemset Mining Using Partition Utility List Structure." Journal of Computational and Theoretical Nanoscience 15, no. 1 (2018): 171–78. http://dx.doi.org/10.1166/jctn.2018.7070.

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Hong, Tzung-Pei, Cho-Han Lee, and Shyue-Liang Wang. "Effective utility mining with the measure of average utility." Expert Systems with Applications 38, no. 7 (2011): 8259–65. http://dx.doi.org/10.1016/j.eswa.2011.01.006.

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Duong, Quang-Huy, Philippe Fournier-Viger, Heri Ramampiaro, Kjetil Nørvåg, and Thu-Lan Dam. "Efficient high utility itemset mining using buffered utility-lists." Applied Intelligence 48, no. 7 (2017): 1859–77. http://dx.doi.org/10.1007/s10489-017-1057-2.

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Wang, Le, and Shui Wang. "HUIL-TN & HUI-TN: Mining high utility itemsets based on pattern-growth." PLOS ONE 16, no. 3 (2021): e0248349. http://dx.doi.org/10.1371/journal.pone.0248349.

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In recent years, high utility itemsets (HUIs) mining has been an active research topic in data mining. In this study, we propose two efficient pattern-growth based HUI mining algorithms, called High Utility Itemset based on Length and Tail-Node tree (HUIL-TN) and High Utility Itemset based on Tail-Node tree (HUI-TN). These two algorithms avoid the time-consuming candidate generation stage and the need of scanning the original dataset multiple times for exact utility values. A novel tree structure, named tail-node tree (TN-tree) is proposed as a key element of our algorithms to maintain complet

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Meruva, Subba Reddy, and Bondu Venkateswarlu. "Dynamic Association Mining Techniques for the Faster Extraction of High Utility Itemsets from Incremental Databases." Engineering, Technology & Applied Science Research 15, no. 1 (2025): 19396–400. https://doi.org/10.48084/etasr.9295.

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Financial and market analysis applications require the mining of strong-utility itemsets. Finding frequent itemsets with high utility patterns is vital for such wide applications. Recent utility-based mining methods were successfully used in the current study to identify high value itemsets from static datasets. Stream databases or incremental databases update the itemsets at regular intervals (schedulers). Incremental Mining-based High Utility Itemset (IM-HUI) algorithms improve the methodologies based on High Utility Itemset (HUI) methods. The proposed technique refines the itemset values an

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Cheng, Haodong, Meng Han, Ni Zhang, Xiaojuan Li, and Le Wang. "A Survey of incremental high-utility pattern mining based on storage structure." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 841–66. http://dx.doi.org/10.3233/jifs-202745.

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Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed

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Sharma, Vikrant. "Improving Efficiency of High Utility Sequential Pattern Extraction." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 234–42. http://dx.doi.org/10.17762/msea.v70i1.2304.

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Text mining used on texts and publications in the biomedical and molecular biology fields is referred to as "biomedical text mining." It is a relatively new area of study at the intersection of computational linguistics, bioinformatics, and natural language processing. Superior usefulness the goal of sequential pattern mining is to identify statistically significant patterns among data instances when the values are presented sequentially. Time series mining is typically regarded as a distinct activity even if it is closely linked since it is typically assumed that the values are discrete. Stru

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Wang, Le, Shui Wang, Haiyan Li, and Chunliang Zhou. "Improved Strategy for High-Utility Pattern Mining Algorithm." Mathematical Problems in Engineering 2020 (November 26, 2020): 1–11. http://dx.doi.org/10.1155/2020/1971805.

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High-utility pattern mining is a research hotspot in the field of pattern mining, and one of its main research topics is how to improve the efficiency of the mining algorithm. Based on the study on the state-of-the-art high-utility pattern mining algorithms, this paper proposes an improved strategy that removes noncandidate items from the global header table and local header table as early as possible, thus reducing search space and improving efficiency of the algorithm. The proposed strategy is applied to the algorithm EFIM (EFficient high-utility Itemset Mining). Experimental verification wa

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Duong, Tran Huy, Demetrovics Janos, Vu Duc Thi, Nguyen Truong Thang, and Tran The Anh. "An Algorithm for Mining High Utility Sequential Patterns with Time Interval." Cybernetics and Information Technologies 19, no. 4 (2019): 3–16. http://dx.doi.org/10.2478/cait-2019-0032.

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Abstract Mining High Utility Sequential Patterns (HUSP) is an emerging topic in data mining which attracts many researchers. The HUSP mining algorithms can extract sequential patterns having high utility (importance) in a quantitative sequence database. In real world applications, the time intervals between elements are also very important. However, recent HUSP mining algorithms cannot extract sequential patterns with time intervals between elements. Thus, in this paper, we propose an algorithm for mining high utility sequential patterns with the time interval problem. We consider not only seq

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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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Mrs., Shweta A. Dubey* Prof. Kemal. Koche. "A SURVEY PAPER ON HIGH UTILITY ITEMSETS MINING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 852–57. https://doi.org/10.5281/zenodo.52492.

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An important data mining task that has received considerable research attention in recent years is the discovery of association rules from the transactional databases. Recently, Utility mining plays a vital role in data mining. To discover high utility itemset from transactional database means discovering item sets with high profits. In this survey paper, we discuss about various methods and algorithms which were used for recovering high utility itemsets from a large database without losing large amount of information.We present different kind of algorithm such as CHUD(Closed High Utility Item

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Ancy, Jose* Dr. John T. Abraham. "A SURVEY ON ITEMSET MINING FOR LARGE TRANSACTION DATABASE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 913–16. https://doi.org/10.5281/zenodo.52500.

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Mining itemsets from the databases is an important data mining task.Frequent itemset mining refers to the mining of set of items occur frequently in the database.Utility itemset mining refers to the discovery of items with high utilities.   Many algorithms have been proposed for mining frequent item sets as well as utility item set.This paper focus on different algorithms and techniques for high utility itemset mining and frequent itemset mining which can handle large transactions in the database.

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Zhang, Mengjiao, Tiantian Xu, Zhao Li, Xiqing Han, and Xiangjun Dong. "e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules." Symmetry 12, no. 8 (2020): 1211. http://dx.doi.org/10.3390/sym12081211.

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As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calcu

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Yang, Yang, Jiaman Ding, Honghai Wang, Huifen Xing, and En Li. "A High Utility Itemset Mining Algorithm Based on Particle Filter." Mathematical Problems in Engineering 2023 (February 23, 2023): 1–15. http://dx.doi.org/10.1155/2023/7941673.

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High utility itemset mining is an interesting research in the field of data mining, which can find more valuable information than frequent itemset mining. Several high-utility itemset mining approaches have already been proposed; however, they have high computational costs and low efficiency. To solve this problem, a high-utility itemset mining algorithm based on the particle filter is proposed. This approach first initializes a population, which consists of particle sets. Then, to update the particle sets and their weights, a novel state transition model is suggested. Finally, the approach al

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Muralidhar, A., and Pattabiraman Venkatasubbu. "HUPM-MUO: high utility pattern mining under multiple utility objectives." International Journal of Computer Aided Engineering and Technology 14, no. 3 (2021): 385. http://dx.doi.org/10.1504/ijcaet.2021.114494.

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Singh, Kuldeep, Shashank Sheshar Singh, Ajay Kumar, and Bhaskar Biswas. "High utility itemsets mining with negative utility value: A survey." Journal of Intelligent & Fuzzy Systems 35, no. 6 (2018): 6551–62. http://dx.doi.org/10.3233/jifs-18965.

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V, PATTABIRAMAN, and Muralidhar A. "HUPM-MUO: High Utility Pattern Mining under Multiple Utility Objectives." International Journal of Computer Aided Engineering and Technology 14, no. 3 (2021): 1. http://dx.doi.org/10.1504/ijcaet.2021.10030789.

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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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Agarwal, Reshu. "Ordering Policy Estimation for High Utility Item-Sets Considering Negative Item Values in Large Databases." International Journal of Decision Support System Technology 14, no. 1 (2022): 1–16. http://dx.doi.org/10.4018/ijdsst.286682.

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Utility mining with negative item values has recently received interest in the data mining field due to its practical considerations. Previously, the values of utility item-sets have been taken into consideration as positive. However, in real-world applications an item-set may be related to negative item values. This paper presents a method for redesigning the ordering policy by including high utility item-sets with negative items. Initially, utility mining algorithm is used to find high utility item-sets. Then, ordering policy is estimated for high utility items considering defective and non-

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Gan, Wensheng, Jerry Chun-Wei Lin, Jiexiong Zhang, et al. "Utility Mining Across Multi-Dimensional Sequences." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (2021): 1–24. http://dx.doi.org/10.1145/3446938.

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Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented mining framework integrates the utility theory to provide more informative and useful patterns. Time-dependent sequence data are commonly seen in real life. Sequence data have been widely utilized in many applications, such as analyzing sequential user behavior on the Web, influence maximization, route planning, and targeted marketing. Unfortunately

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Sultana, Arshia, and Mrs E. Krishnaveni Reddy. "Up Approach: Mining High Utility Itemsets." International Journal of Computer & Orgnanization Trends 10, no. 1 (2014): 1–10. http://dx.doi.org/10.14445/22492593/ijcot-v10p301.

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Song, Wei, Guibin Ren, and Wensheng Gan. "Fast mining local high-utility itemsets." Engineering Applications of Artificial Intelligence 145 (April 2025): 109960. https://doi.org/10.1016/j.engappai.2024.109960.

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