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Relevant bibliographies by topics / Data Mining; Frequent pattern Mining; Utility Mining

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Academic literature on the topic 'Data Mining; Frequent pattern Mining; Utility Mining'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Data Mining; Frequent pattern Mining; Utility Mining"

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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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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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Madan Kumar, K. M. V., and B. Srinivasa Rao. "Mining Frequent Utility Sequential Patterns in Progressive Databases by U-Pisa." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1786–95. http://dx.doi.org/10.1166/jctn.2020.8442.

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Sequential pattern mining is one of the most important aspects of data mining world and has a significant role in many applications like market analysis, biomedical analysis, weather forecasting etc. In the category of mining sequential patterns the usage of progressive database as an input database is relatively new and has a wide impact in decision-making system. In progressive sequential pattern mining, we discover the frequent sequences progressively with the help of period of Interest. As the traditional approaches of frequency based framework are not much more informative for decision ma

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Muley, Abhinav, and Manish Gudadhe. "Synthesizing High-Utility Patterns from Different Data Sources." Data 3, no. 3 (2018): 32. http://dx.doi.org/10.3390/data3030032.

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In large organizations, it is often required to collect data from the different geographic branches spread over different locations. Extensive amounts of data may be gathered at the centralized location in order to generate interesting patterns via mono-mining the amassed database. However, it is feasible to mine the useful patterns at the data source itself and forward only these patterns to the centralized company, rather than the entire original database. These patterns also exist in huge numbers, and different sources calculate different utility values for each pattern. This paper proposes

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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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Ghaib, Arkan A., Yahya Eneid Abdulridha Alsalhi, Israa M. Hayder, Hussain A. Younis, and Abdullah A. Nahi. "Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data." Journal of Computer Science and Technology Studies 5, no. 4 (2023): 122–31. http://dx.doi.org/10.32996/jcsts.2023.5.4.12.

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High utility pattern mining is an analytical approach used to identify sets of items that exceed a specific threshold of utility values. Unlike traditional frequency-based analysis, this method considers user-specific constraints like the number of units and benefits. In recent years, the importance of making informed decisions based on utility patterns has grown significantly. While several utility-based frequent pattern extraction techniques have been proposed, they often face limitations in handling large datasets. To address this challenge, we propose an optimized method called improving t

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Li, Jinhong, Lizhen Wang, Hongmei Chen, and Zhengbao Sun. "Mining spatial high-average utility co-location patterns from spatial data sets." Intelligent Data Analysis 26, no. 4 (2022): 911–31. http://dx.doi.org/10.3233/ida-215848.

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The spatial co-location pattern refers to a subset of non-empty spatial features whose instances are frequently located together in a spatial neighborhood. Traditional spatial co-location pattern mining is mainly based on the frequency of the pattern, and there is no difference in the importance or value of each spatial feature within the pattern. Although the spatial high utility co-location pattern mining solves this problem, it does not consider the effect of pattern length on the utility. Generally, the utility of the pattern also increases as the length of the pattern increases. Therefore

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Mangesh Ghonge, Prof, and Miss Neha Rane. "Mining Rare Patterns by Using Automated Threshold Support." International Journal of Engineering & Technology 7, no. 3.8 (2018): 77. http://dx.doi.org/10.14419/ijet.v7i3.8.15225.

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Essentially the most primary and crucial part of data mining is pattern mining. For acquiring important corre-lations among the information, method called itemset mining plays vital role Earlier, the notion of itemset mining was used to acquire the absolute most often occurring items in the itemset. In some situation, though having utility value less than threshold it is necessary to locate such items because they are of great use. Considering the thought of weight for each and every apparent items brings effectiveness for mining the pattern efficiently. Different mining algorithms are utilize

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Lin, Jerry Chun-Wei, Youcef Djenouri, Gautam Srivastava, Yuanfa Li, and Philip S. Yu. "Scalable Mining of High-Utility Sequential Patterns With Three-Tier MapReduce Model." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (2022): 1–26. http://dx.doi.org/10.1145/3487046.

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High-utility sequential pattern mining (HUSPM) is a hot research topic in recent decades since it combines both sequential and utility properties to reveal more information and knowledge rather than the traditional frequent itemset mining or sequential pattern mining. Several works of HUSPM have been presented but most of them are based on main memory to speed up mining performance. However, this assumption is not realistic and not suitable in large-scale environments since in real industry, the size of the collected data is very huge and it is impossible to fit the data into the main memory o

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Dissertations / Theses on the topic "Data Mining; Frequent pattern Mining; Utility Mining"

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Shang, Xuequn. "SQL based frequent pattern mining." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975449176.

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Jiang, Fan. "Frequent pattern mining of uncertain data streams." Springer-Verlag, 2011. http://hdl.handle.net/1993/5233.

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When dealing with uncertain data, users may not be certain about the presence of an item in the database. For example, due to inherent instrumental imprecision or errors, data collected by sensors are usually uncertain. In various real-life applications, uncertain databases are not necessarily static, new data may come continuously and at a rapid rate. These uncertain data can come in batches, which forms a data stream. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, algorithms have been developed to use the sliding window model for processing and

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Yun, Unil. "New approaches to weighted frequent pattern mining." Texas A&M University, 2005. http://hdl.handle.net/1969.1/5003.

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Researchers have proposed frequent pattern mining algorithms that are more efficient than previous algorithms and generate fewer but more important patterns. Many techniques such as depth first/breadth first search, use of tree/other data structures, top down/bottom up traversal and vertical/horizontal formats for frequent pattern mining have been developed. Most frequent pattern mining algorithms use a support measure to prune the combinatorial search space. However, support-based pruning is not enough when taking into consideration the characteristics of real datasets. Additionally, after mi

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Liu, Guimei. "Supporting efficient and scalable frequent pattern mining /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20LIUG.

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Jiang, Fan. "Efficient frequent pattern mining from big data and its applications." Springer, 2014. http://hdl.handle.net/1993/32083.

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Frequent pattern mining is an important research areas in data mining. Since its introduction, it has drawn attention of many researchers. Consequently, many algorithms have been proposed. Popular algorithms include level-wise Apriori based algorithms, tree based algorithms, and hyperlinked array structure based algorithms. While these algorithms are popular and beneficial due to some nice properties, they also suffer from some drawbacks such as multiple database scans, recursive tree constructions, or multiple hyperlink adjustments. In the current era of big data, high volumes of a wide varie

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Cederquist, Aaron. "Frequent Pattern Mining among Weighted and Directed Graphs." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123.

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Silvestri, Claudio <1974&gt. "Distributed and stream data mining algorithms for frequent pattern discovery." Doctoral thesis, Università Ca' Foscari Venezia, 2006. http://hdl.handle.net/10579/143.

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Maden, Engin. "Data Mining On Architecture Simulation." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611635/index.pdf.

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Data mining is the process of extracting patterns from huge data. One of the branches in data mining is mining sequence data and here the data can be viewed as a sequence of events and each event has an associated time of occurrence. Sequence data is modelled using episodes and events are included in episodes. The aim of this thesis work is analysing architecture simulation output data by applying episode mining techniques, showing the previously known relationships between the events in architecture and providing an environment to predict the performance of a program in an architecture before

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King, Stuart. "Optimizations and applications of Trie-Tree based frequent pattern mining." Diss., Connect to online resource - MSU authorized users, 2006.

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Thesis (M. S.)--Michigan State University. Dept. of Computer Science and Engineering, 2006.<br>Title from PDF t.p. (viewed on June 19, 2009) Includes bibliographical references (p. 79-80). Also issued in print.

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MacKinnon, Richard Kyle. "Seeing the forest for the trees: tree-based uncertain frequent pattern mining." Springer International Publishing, 2014. http://hdl.handle.net/1993/31059.

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Many frequent pattern mining algorithms operate on precise data, where each data point is an exact accounting of a phenomena (e.g., I have exactly two sisters). Alas, reasoning this way is a simplification for many real world observations. Measurements, predictions, environmental factors, human error, &ct. all introduce a degree of uncertainty into the mix. Tree-based frequent pattern mining algorithms such as FP-growth are particularly efficient due to their compact in-memory representations of the input database, but their uncertain extensions can require many more tree nodes. I propose new

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Books on the topic "Data Mining; Frequent pattern Mining; Utility Mining"

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Indian Institute of Management, Ahmedabad., ed. An efficient algorithm for frequent pattern mining for real-time business intelligence analytics in dense datasets. Indian Institute of Management, 2005.

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Aggarwal, Charu C., and Jiawei Han. Frequent Pattern Mining. Springer London, Limited, 2014.

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Aggarwal, Charu C., and Jiawei Han. Frequent Pattern Mining. Springer, 2016.

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Frequent Pattern Mining. Springer, 2014.

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Aydin, Berkay, and Rafal A. Angryk. Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories. Springer, 2018.

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“Data Mining Concepts & Techniques”. 3rd ed. Morgan Kaufmann Publishers, 2011.

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Book chapters on the topic "Data Mining; Frequent pattern Mining; Utility Mining"

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Qamar, Usman, and Muhammad Summair Raza. "Frequent Pattern Mining." In Data Science Concepts and Techniques with Applications. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17442-1_9.

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Anastasiu, David C., Jeremy Iverson, Shaden Smith, and George Karypis. "Big Data Frequent Pattern Mining." In Frequent Pattern Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_10.

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Lee, Victor E., Ruoming Jin, and Gagan Agrawal. "Frequent Pattern Mining in Data Streams." In Frequent Pattern Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_9.

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Zimek, Arthur, Ira Assent, and Jilles Vreeken. "Frequent Pattern Mining Algorithms for Data Clustering." In Frequent Pattern Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_16.

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Toivonen, Hannu. "Frequent Pattern." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_318.

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Bramer, Max. "Association Rule Mining III: Frequent Pattern Trees." In Principles of Data Mining. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7307-6_18.

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Bramer, Max. "Association Rule Mining III: Frequent Pattern Trees." In Principles of Data Mining. Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-7493-6_18.

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Bramer, Max. "Association Rule Mining III: Frequent Pattern Trees." In Principles of Data Mining. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4884-5_18.

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Shahbazi, Nima, Rohollah Soltani, and Jarek Gryz. "Memory Efficient Frequent Itemset Mining." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96133-0_2.

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Pasquier, Claude, Jérémy Sanhes, Frédéric Flouvat, and Nazha Selmaoui-Folcher. "Frequent Pattern Mining in Attributed Trees." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37453-1_3.

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Conference papers on the topic "Data Mining; Frequent pattern Mining; Utility Mining"

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Wu, Jimmy Ming-Tai, and Yadong Liu. "Elite Frequent-Utility Pattern Mining in Hadoop Environments." In 2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2023. http://dx.doi.org/10.1109/icaibd57115.2023.10206354.

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Li, Feng-gang, Ying-jia Sun, Zhi-wei Ni, Yu Liang, and Xue-ming Mao. "The Utility Frequent Pattern Mining Based on Slide Window in Data Stream." In 2012 Fifth International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2012. http://dx.doi.org/10.1109/icicta.2012.110.

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Wu, Jimmy Ming-Tai, Huiying Zhou, Jerry Chun-Wei Lin, Ke Wang, Shuo Liu, and Ranran Li. "A Novel Spark-Based Algorithm for Mining Frequent Utility Patterns." In 2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2023. http://dx.doi.org/10.1109/icaibd57115.2023.10206044.

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Wu, Jimmy Ming-Tai, Yadong Liu, Huiying Zhou, Fengyang Li, and Shaowei Ma. "Mining Skyline Frequent-Utility Pattern with Threshold Filtering." In 2023 8th International Conference on Computer and Communication Systems (ICCCS). IEEE, 2023. http://dx.doi.org/10.1109/icccs57501.2023.10151123.

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Aggarwal, Charu C., Yan Li, Jianyong Wang, and Jing Wang. "Frequent pattern mining with uncertain data." In the 15th ACM SIGKDD international conference. ACM Press, 2009. http://dx.doi.org/10.1145/1557019.1557030.

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Borgwardt, Karsten, Hans-peter Kriegel, and Peter Wackersreuther. "Pattern Mining in Frequent Dynamic Subgraphs." In Sixth International Conference on Data Mining (ICDM'06). IEEE, 2006. http://dx.doi.org/10.1109/icdm.2006.124.

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Helmi, Shahab, and Farnoush Banaei-Kashani. "Multiscale Frequent Co-movement Pattern Mining." In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. http://dx.doi.org/10.1109/icde48307.2020.00077.

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Gan, Wensheng, Jerry Chun-Wei, Han-Chieh Chao, Tzung-Pei Hong, and Philip S. Yu. "CoUPM: Correlated Utility-based Pattern Mining." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622242.

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Aditya, S. P., M. Hemanth, C. K. Lakshmikanth, and K. R. Suneetha. "Effective algorithm for frequent pattern mining." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8389527.

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Tanbeer, Syed Khairuzzaman, Chowdhury Farhan Ahmed, Byeong-Soo Jeong, and Young-Koo Lee. "Efficient frequent pattern mining over data streams." In Proceeding of the 17th ACM conference. ACM Press, 2008. http://dx.doi.org/10.1145/1458082.1458326.

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