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

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Published: 3 June 2025

Last updated: 31 July 2025

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1

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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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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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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Xie, Shiyong, and Long Zhao. "An Efficient Algorithm for Mining Stable Periodic High-Utility Sequential Patterns." Symmetry 14, no. 10 (2022): 2032. http://dx.doi.org/10.3390/sym14102032.

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Periodic high-utility sequential pattern mining (PHUSPM) is used to extract periodically occurring high-utility sequential patterns (HUSPs) from a quantitative sequence database according to a user-specified minimum utility threshold (minutil). A sequential pattern’s periodicity is determined by measuring when the frequency of its periods (the time between two consecutive happenings of the sequential pattern) exceed a user-specified maximum periodicity threshold (maxPer). However, due to the strict judgment threshold, the traditional PHUSPM method has the problem that some useful sequential pa

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Wang, Weiya, Geng Yang, Lin Bao, Ke Ma, Hao Zhou, and Yunlu Bai. "Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection." Wireless Communications and Mobile Computing 2021 (August 5, 2021): 1–14. http://dx.doi.org/10.1155/2021/6379530.

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Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph

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Gupta, Pankaj, and Bharat Bhushan Sagar. "Determining Weighted, Utility-Based Time Variant Association Rules Using Frequent Pattern Tree." Ingeniería Solidaria 14, no. 25 (2018): 1–11. http://dx.doi.org/10.16925/.v14i0.2228.

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Introduction: The present research was conducted at Birla Institute of Technology, off Campus in Noida, India, in 2017.Methods: To assess the efficiency of the proposed approach for information mining a method and an algorithm were proposed for mining time-variant weighted, utility-based association rules using fp-tree.Results: A method is suggested to find association rules on time-oriented frequency-weighted, utility-based data, employing a hierarchy for pulling-out item-sets and establish their association.Conclusions: The dimensions adopted while developing the approach compressed a large

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G.V.S. Nandini and Dr. N. K. Kameswara Rao. "Utility Frequent Patterns Mining on Large Scale Data based on Appriori MapReduce Algorithm." International Journal of Research in Informative Science Application & Techniques (IJRISAT) 3, no. 8 (2019): 1–7. http://dx.doi.org/10.46828/ijrisat.v3i8.111.

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Pattern mining is a standout amongst the majority essential responsibilities to separate significant and helpful data from unprocessed data. Here the work intends to separate itemsets are speak to a homogeneity and consistency in data. At present techniques have been produced in such manner; the developing enthusiasm for data have cause of execution of presented Pattern Mining procedures to be drop. The objective of article, to enhance new productive “PM Algorithms” to work on huge data. At this situation, a progression of techniques dependent on MapReduce structure and the hadoop environment

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Sara salman Qasim and Lubna Mohammed Hasan. "Mining Utilities Itemsets based on social network." Babylonian Journal of Networking 2024 (March 3, 2024): 25–30. http://dx.doi.org/10.58496/bjn/2024/004.

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Mining utility item sets based on social network data involves extracting meaningful patterns and associations from user interactions. In this paper, the process begins by collecting and preprocessing data from platforms like Facebook, Twitter, or LinkedIn. Utility measures are defined based on frequency of occurrence, user engagement metrics, or other domain-specific criteria. Itemsets that meet certain thresholds are identified using techniques like frequent itemset mining or advanced algorithms like Apriori or FP-growth. Additional analyses, such as association rule mining, uncover relation

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Buffett, Scott. "Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists." Information 11, no. 1 (2020): 44. http://dx.doi.org/10.3390/info11010044.

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A ubiquitous challenge throughout all areas of data mining, particularly in the mining of frequent patterns in large databases, is centered on the necessity to reduce the time and space required to perform the search. The extent of this reduction proportionally facilitates the ability to identify patterns of interest. High utility sequential pattern mining (HUSPM) seeks to identify frequent patterns that are (1) sequential in nature and (2) hold a significant magnitude of utility in a sequence database, by considering the aspect of item value or importance. While traditional sequential pattern

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Nunez-del-Prado, Miguel, Yoshitomi Maehara-Aliaga, Julián Salas, Hugo Alatrista-Salas, and David Megías. "A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns." Applied Sciences 12, no. 4 (2022): 2131. http://dx.doi.org/10.3390/app12042131.

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Currently, individuals leave a digital trace of their activities when they use their smartphones, social media, mobile apps, credit card payments, Internet surfing profile, etc. These digital activities hide intrinsic usage patterns, which can be extracted using sequential pattern algorithms. Sequential pattern mining is a promising approach for discovering temporal regularities in huge and heterogeneous databases. These sequences represent individuals’ common behavior and could contain sensitive information. Thus, sequential patterns should be sanitized to preserve individuals’ privacy. Hence

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Wu, Jimmy, Ranran Li, Pi-Chung Hsu, and Mu-En Wu. "The effective skyline quantify-utility patterns mining algorithm with pruning strategies." Computer Science and Information Systems, no. 00 (2023): 40. http://dx.doi.org/10.2298/csis220615040w.

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Frequent itemsetmining and high-utility itemsetmining have been widely applied to the extraction of useful information from databases. However, with the proliferation of the Internet of Things, smart devices are generating vast amounts of data daily, and studies focusing on individual dimensions are increasingly unable to support decision-making. Hence, the concept of a skyline query considering frequency and utility (which returns a set of points that are not dominated by other points) was introduced. However, in most cases, firms are concerned about not only the frequency of purchases but al

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Vy, Huynh Trieu, Le Quoc Hai, Nguyen Thanh Long, Truong Ngoc Chau, and Le Quoc Hieu. "Hiding Sensitive High Utility and Frequent Itemsets Based on Constrained Intersection Lattice." Cybernetics and Information Technologies 22, no. 1 (2022): 3–23. http://dx.doi.org/10.2478/cait-2022-0001.

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Abstract Hiding high utility and frequent itemset is the method used to preserve sensitive knowledge from being revealed by pattern mining process. Its goal is to remove sensitive high utility and frequent itemsets from a database before sharing it for data mining purposes while minimizing the side effects. The current methods succeed in the hiding goal but they cause high side effects. This paper proposes a novel algorithm, named HSUFIBL, that applies a heuristic for finding victim item based on the constrained intersection lattice theory. This algorithm specifies exactly the condition that a

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Jagtap, Nitin Pundlik. "The Hash base Apriori Technique for Association Rule Mining and Data Sanitization." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, no. 04 (2022): 24–29. http://dx.doi.org/10.18090/samriddhi.v14i04.04.

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Under different circumstances, private information is exposed, and it must be sanitised before even being shared to address privacy issues. Data mining techniques can collect large amounts of data in a short amount of time. The information gathered by the powerful machine learning techniques may identify the most sensitive content, which pertains to an individual or organization. The degree of sensitivity of data belonging to a business or an agency might vary. Only approved individuals and organizations have access to this information. As a result, using access limitations to confirm the secu

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Mohbey, Krishna Kumar, and Sunil Kumar. "A parallel approach for high utility-based frequent pattern mining in a big data environment." Iran Journal of Computer Science 4, no. 3 (2021): 195–200. http://dx.doi.org/10.1007/s42044-021-00083-5.

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Gopagoni, Praveen Kumar, and Mohan Rao S K. "Distributed elephant herding optimization for grid-based privacy association rule mining." Data Technologies and Applications 54, no. 3 (2020): 365–82. http://dx.doi.org/10.1108/dta-07-2019-0104.

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PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the rese

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Gulzar, Kanza, Muhammad Ayoob Memon, Syed Muhammad Mohsin, Sheraz Aslam, Syed Muhammad Abrar Akber, and Muhammad Asghar Nadeem. "An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young Adults." Information 14, no. 4 (2023): 203. http://dx.doi.org/10.3390/info14040203.

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In the public health sector and the field of medicine, the popularity of data mining and its usage in knowledge discovery and databases (KDD) are rising. The growing popularity of data mining has discovered innovative healthcare links to support decision making. For this reason, there is a great possibility to better diagnose patient’s diseases and maintain the quality of healthcare services in hospitals. So, there is an urgent need to make disease diagnosis possible by discovering the hidden patterns from the patients’ history information in developing countries. This work is a step towards h

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Lin, Jessica, Eamonn Keogh, and Stefano Lonardi. "Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases." Information Visualization 4, no. 2 (2005): 61–82. http://dx.doi.org/10.1057/palgrave.ivs.9500089.

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Data visualization techniques are very important for data analysis, since the human eye has been frequently advocated as the ultimate data-mining tool. However, there has been surprisingly little work on visualizing massive time series data sets. To this end, we developed VizTree, a time series pattern discovery and visualization system based on augmenting suffix trees. VizTree visually summarizes both the global and local structures of time series data at the same time. In addition, it provides novel interactive solutions to many pattern discovery problems, including the discovery of frequent

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Lu, Kai, Alireza Khani, and Baoming Han. "A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data." Complexity 2018 (August 28, 2018): 1–14. http://dx.doi.org/10.1155/2018/3412070.

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Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information.

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San Yap, Kian, and Tieng Wei Koh. "DATA ANALYTICS PREDICTIVE MODEL TO PROMOTE UP-SELLING AND CROSS-SELLING ACTIVITIES BASED ON CONSUMER’S BUYING PATTERN." Platform A Journal of Science and Technology 8, no. 1 (2025): 23. https://doi.org/10.61762/pjstvol8iss1art003.

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In the current competitive market, companies need to utilise data-driven approaches to maximise sales and improve customer interaction. This research intends to create a predictive analytics and recommendation system to enhance up-selling and cross-selling efforts for dealers. By employing machine learning models such as linear regression, random forest, and extreme gradient boosting (XGBoost), the system predicts product demand, facilitating improved inventory control and sales enhancement. Moreover, an association rule mining (Apriori algorithm) method is utilised to uncover frequent item as

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Rajendra, Chouhan, Khushboo Sawant Er., and Harish Patidar Dr. "A Systematic Literature Review of Frequent Pattern Mining Techniques." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2223–26. https://doi.org/10.31142/ijtsrd11670.

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Mining of frequent items from a voluminous storage of data is the most favorite topic over the years. Frequent pattern mining has a wide range of real world applications market basket analysis is one of them. In this paper, we present an overview of modern frequent pattern mining techniques using data mining algorithms. Frequent pattern mining in data mining takes a lot of data base scans. Therefore it is a computationally expensive task. So still there is a need to update and enhance the existing frequent pattern mining techniques so that we can get the more efficient methods for the same tas

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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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Singh, Gauravjeet, Sandeep Bal, Poonamjeet Kaur, and Kanwaljit Kaur. "Comparative Study of Frequent Pattern Mining Techniques." Advanced Materials Research 403-408 (November 2011): 1022–27. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1022.

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Frequent pattern mining has been a focused theme in data mining research. Lots of techniques have been proposed to improve the performance of frequent pattern mining algorithms. This paper presents review of different frequent mining techniques. With each technique, we have provided brief description of the technique. At the end, we compared different frequent pattern mining techniques.

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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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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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S, Abirami. "Pattern-Growth Methods for Frequent Pattern Mining." Shanlax International Journal of Arts, Science and Humanities 6, S1 (2018): 76–81. https://doi.org/10.5281/zenodo.1410989.

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Mining frequent patterns from large databases play an essential role in many data mining tasks and has broad applications. Most of the previously proposed methods adopt Apriori-like candidate-generation-and-test approaches. However, those methods  may  encounter  serious  challenges  when  mining  datasets  with  prolific patterns and long patterns.In this work, to develop a class of novel and efficient pattern-growth methods for mining various frequent patterns from large databases. Pattern-growth methods  adopt a divi

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KRIBII, Rajae, and Youssef FAKIR. "Mining Frequent Sequential Patterns." Journal of Big Data Research 1, no. 2 (2021): 20–37. http://dx.doi.org/10.14302/issn.2768-0207.jbr-21-3455.

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In recent times, the urge to collect data and analyze it has grown. Time stamping a data set is an important part of the analysis and data mining as it can give information that is more useful. Different mining techniques have been designed for mining time-series data, sequential patterns for example seeks relationships between occurrences of sequential events and finds if there exist any specific order of the occurrences. Many Algorithms has been proposed to study this data type based on the apriori approach. In this paper we compare two basic sequential algorithms which are General Sequentia

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Ge, Cui Cui, and Xiu Fen Fu. "Mining Closed Weighed Frequent Patterns from a Sliding Window over Data Stream." Advanced Materials Research 756-759 (September 2013): 2606–9. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2606.

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Weighted frequent pattern mining address to discover more important frequent pattern by considering different weights of every item, closed frequent pattern mining can significantly reduce the number of frequent itemset mining and keep sufficient result information. In this paper,we proposed an algorithm DS_CRWF to mine closed weighted frequent pattern over data stream,which is based on sliding window and take basic window as unit of updating,all the closed weighted frequent patterns can be mined through once scan.The experimental results show the feasibility of the algorithm.

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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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Chen, Chien-Ming, Zhenzhou Zhang, Jimmy Ming-Tai Wu, and Kuruva Lakshmanna. "High Utility Periodic Frequent Pattern Mining in Multiple Sequences." Computer Modeling in Engineering & Sciences 137, no. 1 (2023): 733–59. http://dx.doi.org/10.32604/cmes.2023.027463.

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Mudasir, Shafi*1 Sumiran Daiya2 and Sumit Dalal3. "MINING INTERNET OF THINGS DATA." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 5 (2018): 370–74. https://doi.org/10.5281/zenodo.1247118.

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A smart world, like ours, is primarily based on the concept of Internet of Things (IoT). IoT generates huge amount on data on a daily basis. It is very important to work on this generated data so as make good use of it. Data mining is essentially used for this purpose. This paper discusses how data mining can be implemented on IoT data. Data mining primarily includes classification (grouping data), clustering (labeling data), frequent pattern mining (finding frequently occurring itemsets or sequences or substructures in data) and outlier analysis (analyzing data with abnormal value of attribut

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Zhao, Ming Ru, Yuan Sun, Jian Guo, and Ping Ping Dong. "Research into the Algorithm of Frequent Pattern Mining Based on across Linker." Applied Mechanics and Materials 195-196 (August 2012): 984–86. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.984.

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Frequent itemsets mining is an important data mining task and a focused theme in data mining research. Apriori algorithm is one of the most important algorithm of mining frequent itemsets. However, the Apriori algorithm scans the database too many times, so its efficiency is relatively low. The paper has therefore conducted a research on the mining frequent itemsets algorithm based on a across linker. Through comparing with the classical algorithm, the improved algorithm has obvious advantages.

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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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Xue, Linyan, Xiaoke Zhang, Fei Xie, Shuang Liu, and Peng Lin. "Frequent Patterns Algorithm of Biological Sequences based on Pattern Prefix-tree." International Journal of Computers Communications & Control 14, no. 4 (2019): 574–89. http://dx.doi.org/10.15837/ijccc.2019.4.3607.

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In the application of bioinformatics, the existing algorithms cannot be directly and efficiently implement sequence pattern mining. Two fast and efficient biological sequence pattern mining algorithms for biological single sequence and multiple sequences are proposed in this paper. The concept of the basic pattern is proposed, and on the basis of mining frequent basic patterns, the frequent pattern is excavated by constructing prefix trees for frequent basic patterns. The proposed algorithms implement rapid mining of frequent patterns of biological sequences based on pattern prefix trees. In e

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Aida Jusoh, Julaily, Mustafa Man, and Wan Aezwani Wan Abu Bakar. "Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset." International Journal of Engineering & Technology 7, no. 4.1 (2018): 134. http://dx.doi.org/10.14419/ijet.v7i4.1.28241.

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Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occurrences may offer valuable information in knowledg

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Mi, Xifeng. "The Mining Algorithm of Maximum Frequent Itemsets Based on Frequent Pattern Tree." Computational Intelligence and Neuroscience 2022 (May 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/7022168.

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In the discipline of data mining, association rule mining is an important study topic that focuses on discovering the relationships between database attributes. The maximum frequent itemset comprises the information of all frequent itemsets, which is one of the important difficulties in mining association rules, and certain data mining applications just need to mine the maximum frequent itemsets. As a result, analyzing the maximum frequent itemset mining technique is practical. Considering this, the research introduces FP-MFIA, a new maximum frequent itemset mining approach based on the FP-tre

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Chuang, Po-Jen, and Yun-Sheng Tu. "Efficient Frequent Pattern Mining in Data Streams." IOP Conference Series: Earth and Environmental Science 234 (March 8, 2019): 012066. http://dx.doi.org/10.1088/1755-1315/234/1/012066.

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Lin, Chun-Cheng, Wei-Ching Li, Ju-Chin Chen, Wen-Yu Chung, Sheng-Hao Chung, and Kawuu W. Lin. "A Distributed Algorithm for Fast Mining Frequent Patterns in Limited and Varying Network Bandwidth Environments." Applied Sciences 9, no. 9 (2019): 1859. http://dx.doi.org/10.3390/app9091859.

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Data mining is a set of methods used to mine hidden information from data. It mainly includes frequent pattern mining, sequential pattern mining, classification, and clustering. Frequent pattern mining is used to discover the correlation among various sets of items within large databases. The rapid upward trend in data size slows the mining of frequent patterns. Numerous studies have attempted to develop algorithms that operate in distributed computing environments to accelerate the mining process. FLR-mining (Fast, Load balancing and Resource efficient mining algorithm) is one of the fastest

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Zhao, Xiao Lei, and Wei Huang. "The Algorithm for Data Mining Frequent Patterns over Sliding Window." Applied Mechanics and Materials 513-517 (February 2014): 759–62. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.759.

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On the basis of the shortcoming of the existed algorithm, this paper probes into sliding windows pattern and introduces an efficient algorithm for data mining frequent pattern over sliding windows. A PSW-tree pattern is set in the algorithm to store frequent and critical pattern in data mining. On this basis, the paper presents a rapid mining algorithmPSW algorithm. In the experiment IBM data generator is used to produce generated data, which proves the validity and better space efficiency of the algorithm.

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Patel, Sanjay, and Dr Ketan Kotecha. "Incremental Frequent Pattern Mining using Graph based approach." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (2005): 731–36. http://dx.doi.org/10.24297/ijct.v4i2c2.4191.

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Extracting useful information from huge amount of data is known as Data Mining. It happens at the intersection of artificial intelligence and statistics. It is also defined as the use of computer algorithms to discover hidden patterns and interesting relationships between items in large datasets. Candidate generation and test, Pattern Growth etc. are the common approaches to find frequent patterns from the database. Incremental mining is a crucial requirement for the industries nowadays. Many tree based approaches have tried to extend the frequent pattern mining as an incremental approach, but

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Dalal, Vandna Dahiya and Sandeep. "Parallel Approaches of Utility Mining for Big Data." Webology 17, no. 2 (2020): 31–43. http://dx.doi.org/10.14704/web/v17i2/web17014.

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Utility Itemset Mining (UIM) is a fundamental technique to find out various itemsets with interestingness measures in addition to their quantity. It helps in finding valuable items that cannot be tracked with frequent itemset mining. There are many techniques to mine the itemsets based on their utilities, but the need of the hour is to mine them from larger datasets. This paper presents a brief overview of various approaches for utility mining, which mine using the parallel framework to enhance the pace of computation. The paper is concluded with a discussion on various challenges and openings

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Harco, Leslie Hendric Spits Warnars, Trisetyarso Agung, and Randriatoamanana Richard. "Confidence of AOI-HEP Mining Pattern." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1217–25. https://doi.org/10.12928/TELKOMNIKA.v16i3.5303.

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Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) has been proven can mine frequent and similar patterns and the finding AOI-HEP patterns will be underlined with confidence mining pattern for each AOI-HEP pattern either frequent or similar pattern, and each dataset as confidence AOIHEP pattern between frequent and similar patterns. Confidence per AOI-HEP pattern will show how interested each of AOI-HEP pattern, whilst confidende per dataset will show how interested each dataset between frequent and similar patterns. The experiments for finding confidence of each AOI-HEP patter

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