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Journal articles on the topic '• Database and Data Mining'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Alkadi, Ihssan. "Data Mining." Review of Business Information Systems (RBIS) 12, no. 1 (2008): 17–24. http://dx.doi.org/10.19030/rbis.v12i1.4394.

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Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Database and information technology have been evolving systematically from primitive file processing systems to sophisticated and powerful databases systems. The research and development in database systems has le

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SUBRAMANYAM, R. B. V., and A. GOSWAMI. "A FUZZY DATA MINING ALGORITHM FOR INCREMENTAL MINING OF QUANTITATIVE SEQUENTIAL PATTERNS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 06 (2005): 633–52. http://dx.doi.org/10.1142/s0218488505003722.

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In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the sequential patterns from databases with binary values. Temporal transactions with quantitative values are commonly seen in real world applications. In addition, several methods have been proposed for representing uncertain data in a database. In this paper, a fuzzy data mining algorithm for incremental mining of sequential patterns from quantitative

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AVeselý. "Neural networks in data mining." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining.

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Muley, Abhinav. "Global Data Fusion versus Local Pattern Fusion in Mining Multiple Databases: A Comparative Review." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 3844–49. http://dx.doi.org/10.1166/jctn.2020.9046.

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With the emergence of big data, mining distributed databases has become a critical task in the domain of discovery of knowledge from databases. Many of the traditional multiple-database mining methods developed until now have emphasized mining the mono-database, which is a pool of all the local databases merged at a central site; local patterns discovered at local sites are not analyzed in mono-database mining. However, in real-world applications, data collected from multiple databases may be duplicitous and unreliable. Therefore, developing methods to discover reliable, high-quality knowledge

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Mariappan, K., G. V. Sriramakrishnan, M. Muthu Selvam, and G. Suseendran. "Data secure in horizontally distributed database using apriori algorithm." International Journal of Engineering & Technology 7, no. 2.31 (2018): 146. http://dx.doi.org/10.14419/ijet.v7i2.31.13428.

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Data mining strategies are utilized as a part of business and explore and are ending up increasingly prominent with time. Information mining can remove valuable data from substantial databases. Most proficient methodologies for mining circulated databases assume that the majority of the information at each site can be shared and conveyed database is use to designate diverse database in various area. Affiliation govern mining is a critical research territory in information mining, which shows relations among thing sets in database[1].The convention, relies upon Fast Distributed Mining (FDM), [2

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Yu, Yong Ling, Tao Guan, and Jin Fa Shi. "Spatial Data Mining Based on Campus GIS." Advanced Materials Research 282-283 (July 2011): 641–45. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.641.

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With the rapid development of spatial database technology, spatial databases have been widely used in many engineering fields and rapidly increase in data capacity. Thus, to mine useful information from large spatial databases turns into a difficult but important task. In this paper, we apply the traditional data mining into spatial database and give a mining model for spatial data based on Campus GIS. Moreover, based on campus GIS, we implement a spatial data mining prototype system that is able to discovery the useful spatial features and patterns in spatial databases. The application in the

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Ambulkar, Bhagyashree, and Prof Gunjan Agre. "Data Mining Over Encrypted Data of Database Client Engine Using Hybrid Classification Approach." International Journal of Innovative Research in Computer Science & Technology 5, no. 3 (2017): 291–94. http://dx.doi.org/10.21276/ijircst.2017.5.3.7.

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B., Suma, and Shobha G. "Privacy preserving association rule hiding using border based approach." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 1137–45. https://doi.org/10.11591/ijeecs.v23.i2.pp1137-1145.

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Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals, and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we in

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Elango, P., K. Kuppusamy, and N. Prabhu. "Data Replication Using Data Mining Techniques." Asian Journal of Computer Science and Technology 8, S1 (2021): 107–9. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1939.

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Database Replication is the successive electronic duplicating of information from a database in one PC or server to a database in another with the goal that all clients share a similar dimension of data. The outcome is a conveyed database in which clients can get to information significant to their assignments without meddling with crafted by others. Anyway information replication is an entrancing theme for both hypothesis and practice. On the hypothetical side, numerous solid outcomes requirement what should be possible as far as consistency: e.g., the difficulty of achieving agreement in off

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Agrawal, Shivangee, and Nivedita Bairagi. "A Survey for Association Rule Mining in Data Mining." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 245. http://dx.doi.org/10.23956/ijarcsse.v7i8.58.

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Data mining, also identified as knowledge discovery in databases has well-known its place as an important and significant research area. The objective of data mining (DM) is to take out higher-level unknown detail from a great quantity of raw data. DM has been used in a variety of data domains. DM can be considered as an algorithmic method that takes data as input and yields patterns, such as classification rules, itemsets, association rules, or summaries, as output. The ’classical’ associations rule issue manages the age of association rules by support portraying a base level of confidence an

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Sun, Xiu Juan. "Preprocessing of Examination Analysis System Data Based on Data Mining." Applied Mechanics and Materials 608-609 (October 2014): 300–303. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.300.

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The development of science and technology has driven the progress of the examination system, through the development of database and intelligent, data mining, the database technology is also developed. But at present there are insufficient data mining scope, so must strengthen the examination analysis system has the function of data preprocessing, data mining to meet the needs of data mining algorithm, and accelerate the execution efficiency of data mining, to prevent the deviation, as a result more than the entire process of data mining and data pretreatment cost and time needed to about fort

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Gunawan, Dedi. "Classification of Privacy Preserving Data Mining Algorithms: A Review." Jurnal Elektronika dan Telekomunikasi 20, no. 2 (2020): 36. http://dx.doi.org/10.14203/jet.v20.36-46.

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Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the da

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Khin, Sein Hlaing, and Myo Kay Khine Thaw Yin. "Applications, Techniques and Trends of Data Mining and Knowledge Discovery Database." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1604–6. https://doi.org/10.5281/zenodo.3591147.

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Data Mining and Knowledge Discovery is intended to be the best technical publication in the field providing a resource collecting relevant common methods and techniques. Traditionally, data mining and knowledge discovery was performed manually. As time passed, the amount of data in many systems grew to larger than terabyte size, and could no longer be maintained manually. Besides, for the successful existence of any business, discovering underlying patterns in data is considered essential. This paper proposed about applications, techniques and trends of Data Mining and Knowledge Discovery Data

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Kumar, Manoj, and Hemant Kumar Soni. "A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining." International Journal of Engineering Research in Africa 23 (April 2016): 120–30. http://dx.doi.org/10.4028/www.scientific.net/jera.23.120.

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Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be inter

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Mathur, Ankita. "Real-Time Data Mining: Applications and Security Challenges." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 2210–24. https://doi.org/10.22214/ijraset.2025.72681.

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Data mining is used to extract knowledge from huge amount of the data Today, Data mining helps different organizations focus on customer’s behavior patterns. The research scope of data mining extended in various fields. This paper, discusses the concept of data mining, important issues and applications. So there comes the need of powerful and most importantly automatic tools for uncovering valuable slots of organized information from tremendous amount of data. Considering social networking site or a search engine, they receive millions of queries every day. Firstly, the Database Management Sys

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Wang, Zhen Chao. "Key Data Optimization Mining in Massive Student Database Model." Applied Mechanics and Materials 687-691 (November 2014): 1466–69. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1466.

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In the process of massive student data mining using traditional method, special words and related characteristics were used as mining objects. The concealment and feature of deliberately camouflaged of information made it is difficult for mining model to form an effective cluster centers, which reduced the accuracy of information mining. Hence an optimized data mining method was proposed. According to the degree of generalization and fuzziness of the feature words of student, the threshold of mining information was set, which avoided the effects of redundant information, thus the efficiency of

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Rahardian, Rifky Lana, and Made Sudarma. "Application of Neural Network Overview In Data Mining." International Journal of Engineering and Emerging Technology 2, no. 1 (2017): 94. http://dx.doi.org/10.24843/ijeet.2017.v02.i01.p19.

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Data Mining is the term used to describe the process extract value / information from the database. Four things are needed in order to effectively data mining: data that has a high quality, right of data, examples of which are adequate, and the correct device. To obtain valuable information in the required data mining algorithms applied in data mining in large databases. There are a lot of complex algorithms in data mining. One is the so-called Neural networks have an important role in data mining.

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Zhang, Zhi Ping, Lin Na Li, Li Jun Wang, and Hai Yan Yu. "A Framework for Object-Oriented Data Mining Based on Higher-Order Logic Programming." Applied Mechanics and Materials 420 (September 2013): 325–32. http://dx.doi.org/10.4028/www.scientific.net/amm.420.325.

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Data mining discovers knowledge and useful information from large amounts of data stored in databases. With the increasing popularity of object-oriented database system in advanced database applications, it is significantly important to study the data mining methods for object-oriented database. This paper proposes that higher-order logic programming languages and techniques is very suitable for object-oriented data mining, and presents a framework for object-oriented data mining based on higher-order logic programming. Such a framework is inductive logic programming which adopts higher-order

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Ushaa, Eswaran. "Optimizing database efficiency: Empowering systems with data mining." i-manager's Journal on Information Technology 12, no. 3 (2023): 32. http://dx.doi.org/10.26634/jit.12.3.20050.

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Databases are critical for storing structured data, but deriving insights remains challenging. This paper investigates integrating classification, clustering, association rules, and anomaly detection within database architectures to enable intelligent analytics. A unified architecture is proposed along with an asynchronous incremental learning technique to efficiently handle dynamic data. Comprehensive experiments on diverse real-world datasets demonstrate 10–25% improvements in metrics like query latency, accuracy, and costs compared to conventional integration approaches. Emerging applicatio

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Rohan Lohia and Vibhor Sharma. "Health Prediction by Data Mining." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 390–93. http://dx.doi.org/10.46501/ijmtst061273.

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The paper presents an overview of the Clinical Predictions and Medical Predictions with data mining and its techniques. In health care areas, due to regulations and due to availability of computers, such large amount of data cannot be processed by humans to schedules and diagnosis in short time of duration. It is a new technology which is of high interest in computer world. The computer world make an data in different databases to transfer into new researches and results. The database management extract a new patterns from large datasets. The different parameters included in data mining are: c

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Kamiya, Yohei, and Hirohisa Seki. "Distributed Mining of Closed Patterns from Multi-Relational Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (2015): 804–9. http://dx.doi.org/10.20965/jaciii.2015.p0804.

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In multi-relational data mining (MRDM), there have been proposed many methods for searching for patterns that involve multiple tables (relations) from a relational database. In this paper, we consider closed pattern mining from distributed multi-relational databases (MRDBs). Since the computation of MRDM is costly compared with the conventional itemset mining, we propose some efficient methods for computing closed patterns using the techniques studied in Inductive Logic Programming (ILP) and Formal Concept Analysis (FCA). Given a set oflocaldatabases, we first compute sets of their closed patt

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Valiullin, Timur, Zhexue Huang, Chenghao Wei, Jianfei Yin, Dingming Wu, and Luliia Egorova. "A new approximate method for mining frequent itemsets from big data." Computer Science and Information Systems, no. 00 (2020): 15. http://dx.doi.org/10.2298/csis200124015v.

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Mining frequent itemsets in transaction databases is an important task in many applications. It becomes more challenging when dealing with a large transaction database because traditional algorithms are not scalable due to the memory limit. In this paper, we propose a new approach for approximately mining of frequent itemsets in a big transaction database. Our approach is suitable for mining big transaction databases since it produces approximate frequent itemsets from a subset of the entire database, and can be implemented in a distributed environment. Our algorithm is able to efficiently pro

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Liu, Haijun, Fan Wang, Yingjie Xiao, et al. "MODEM: multi-omics data envelopment and mining in maize." Database 2016 (2016): baw117. http://dx.doi.org/10.1093/database/baw117.

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Surbhi, Agrawal, and K. Vishwakarma Santosh. "Predicting Student's Academic Performance using Data Mining Techniques." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 215–19. https://doi.org/10.35940/ijeat.B4521.029320.

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To meet the change in world in terms of digitalization and progress, the need and importance of education is known to everyone. The increasing awareness towards and digitization has given rise to increase in size of education field’s database. Such database contains information about students. The information includes students behavior, their family background, the facility they have, the society environment which surrounds them, their academic records etc. The increasing technology in data sciences can help utilize this huge education field database in a productive way by applying data

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Wang, Xindi, Mengfei Chen, and Li Chen. "Research of the Optimization of a Data Mining Algorithm Based on an Embedded Data Mining System." Cybernetics and Information Technologies 13, Special-Issue (2013): 5–17. http://dx.doi.org/10.2478/cait-2013-0033.

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Abstract At present most of the data mining systems are independent with respect to the database system, and data loading and conversion take much time. The running time of the algorithms in a data mining process is also long. Although some optimized algorithms have improved it in different aspects, they could not improve the efficiency to a large extent when many duplicate records are available in a database. Solving the problem of improving the efficiency of data mining in the presence of many coinciding records in a database, an Apriori optimized algorithm is proposed. Firstly, a new concep

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Vijayarani, S., C. Sivamathi, and R. Prassanalakshmi. "Frequent Items Mining on Data Streams using Matrix and Scan Reduced Indexing Algorithms." ASEAN Journal of Science and Engineering 3, no. 2 (2022): 123–38. http://dx.doi.org/10.17509/ajse.v3i2.45345.

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A data stream is used for handling dynamic databases, in which data can arrive continuously without limit. Association rule mining is a data mining technique, used to find the association between the data items in the databases. To generate association rules, frequent items are to be identified from the transactional database. Normally, in data mining, frequent-item-generation algorithms scan the database multiple times. But this is impossible in data streams because it handles dynamic databases. Hence, there is a need to develop a new algorithm, which reduces the number of database scans. In

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Xie, Wu, Huimin Zhang, Bizhong Wei, and Guanghai Fang. "Data mining of graduation project selection database." Procedia Engineering 15 (2011): 4008–11. http://dx.doi.org/10.1016/j.proeng.2011.08.751.

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Rikhi, Nainja. "Data Mining and Knowledge Discovery in Database." International Journal of Engineering Trends and Technology 23, no. 2 (2015): 64–70. http://dx.doi.org/10.14445/22315381/ijett-v23p213.

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Lee, Chin-Feng, S. Wesley Changchien, Wei-Tse Wang, and Jau-Ji Shen. "A data mining approach to database compression." Information Systems Frontiers 8, no. 3 (2006): 147–61. http://dx.doi.org/10.1007/s10796-006-8777-x.

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Tan, Kenneth Lee Shean, and Saharuddin Bin Mohamad. "CFPG: Creating a Common Fungal Pathogenic Genes Database through Data Mining." Chiang Mai Journal of Science 51, no. 3 (2024): 1–11. http://dx.doi.org/10.12982/cmjs.2024.038.

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Fu ngal pathogenicity is one of the most vigorously tackled ecological and medicinal issues facing many scientists. Comparative genomics is an extremely important methodology and tool used to understand fungal pathogenicity, and it allows the development of early diagnostic tools for fungal-inflicted diseases across different host organisms. However, comparative genomics depends heavily on readily available fungal pathogenic gene databases to enable downstream genomics study and the development of new diagnosis and detection methods. Here, we have developed the Common Fungal Pathogenic Genes D

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N., Thinaharan, Chitradevi B., Malathi P., and Kalpana K. "A LITERATURE SURVEY ON DATA MINING TECHNIQUES AND CONCEPTS." International Journal of Engineering Research and Modern Education 3, no. 2 (2018): 1–3. https://doi.org/10.5281/zenodo.1332042.

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Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization. Data mining is the process of analyzing data from different views and summarizing it into useful data. “Data mining, also popularly referred to as knowledge discovery from data (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large data

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S.P., Siddique Ibrahim. "EXTRACT DATA IN LARGE DATABASE WITH HADOOP." International Journal of Advances in Engineering & Scientific Research 1, no. 7 (2014): 05–09. https://doi.org/10.5281/zenodo.10725257.

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<strong><em>Abstract</em></strong><strong><em>:</em></strong> <strong>                  </strong><em>Data is basic building block of any organization and extracting useful information from raw available data is the big task and high complexity task. Data are the patterns which are used to develop or enhance knowledge. The rapid growth in the size of datasets that are collected from different resources has made capturing, managing and analyzing the datasets beyond the ability of most software tools. The current

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Vipin, Kumar, and Sharma Deepti. "Network Security and Data Mining: Comprehensive Review." Journal of Advances in Cryptography and Network Security 1, no. 1 (2025): 1–2. https://doi.org/10.5281/zenodo.14770398.

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<em>Information and databases are defined as support systems that play an important role in information retrieval and use. This study presents the results of eight research projects and investigates security-related technologies such as error detection, encryption, and data mining</em>

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Hu, Yang. "Data Series Mining Technology Analysis Based on Web Database." Applied Mechanics and Materials 687-691 (November 2014): 1304–7. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1304.

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Based on Web data, the paper regarded data series mining technology as research content, researched and realized sequence GSP algorithm with higher mining efficiency on sequence pattern, tested the algorithm and analyzed mining pattern. Moreover, the paper realized a scheme that used data warehouse (DW) to excavate sequence of Web visit to use entirely, tested the scheme by adopting logs of real business sites, analyzed mining results and drew a conclusion that this algorithm had an excellent application effect.

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Amin, Hetal, and Rohit Sharma. "How Data Mining is useful in Ayurveda." Journal of Ayurvedic and Herbal Medicine 2, no. 3 (2016): 61–62. http://dx.doi.org/10.31254/jahm.2016.2301.

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Data mining is a computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems. [1-3] The term data mining appeared around 1990 in the database community. Currently, data mining and knowledge discovery are used interchangeably.

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Zheng, Rui Ying. "Simulation of Data Mining System Design in Database." Advanced Materials Research 989-994 (July 2014): 2020–23. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2020.

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The design of data mining system in database is researched. Vast amounts of information contained in the database, and the data show the diversity of characteristics, resulting in lower efficiency of data mining in database, which database brought greater difficulties to information query. To avoid these shortcomings, database performance optimization method based on cloud computing is proposed. The model of cloud computing data relationship is established to describe the connection between related data inthe database, thus providing the basis for data query. The load state of data nodes is ca

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Wael, Ahmad AlZoubi, Mahmoud Alturani Ibrahim, and Mahmoud Ali Aloglah Roba. "Graph-based methods for transaction databases: a comparative study." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1663–72. https://doi.org/10.11591/ijai.v14.i2.pp1663-1672.

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There has been an increased demand for structured data mining. Graphs are among the most extensively researched data structures in discrete mathematics and computer science. Thus, it should come as no surprise that graph-based data mining has gained popularity in recent years. Graph-based methods for a transaction database are necessary to transform all the information into a graph form to conveniently extract more valuable information to improve the decision-making process. Graph-based data mining can reveal and measure process insights in a detailed structural comparison strategy that is rea

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Kalogeras, I. S., G. Marketos, and Y. Theodoridis. "A TOOL FOR COLLECTING, QUERYING AND MINING MACROSEISMIC DATA." Bulletin of the Geological Society of Greece 36, no. 3 (2004): 1406. http://dx.doi.org/10.12681/bgsg.16509.

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SEISMO-SURFER is a tool for collecting, querying and mining seismic data being developed in Java programming language using Oracle database system. The objective is to combine recent research trends and results in the fields of spatial and spatio-temporal databases, data warehouses and data mining, as well as well established visualization techniques for geographical information. The database of the tool is automatically updated from remote sources while existing possibilities allow the querying on different earthquakes parameters, the analysis of the data for extraction of useful information

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Borysov, Stanislav S., R. Matthias Geilhufe, and Alexander V. Balatsky. "Organic materials database: An open-access online database for data mining." PLOS ONE 12, no. 2 (2017): e0171501. http://dx.doi.org/10.1371/journal.pone.0171501.

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Sun, Xiang Kun. "The Development and Research of Data Mining Technology." Applied Mechanics and Materials 602-605 (August 2014): 3461–64. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3461.

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With the development of computer science and database system, millions of data will be generated every day. How to mining useful information and knowledge from large database is becoming a more and more popular research topic. In this paper, we introduced the origin of data mining, discussed some different data mining techniques in different fields, and made a conclusion of the application of data mining.

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LOTOV, ALEXANDER V. "VISUALIZATION-BASED SELECTION-AIMED DATA MINING WITH FUZZY DATA." International Journal of Information Technology & Decision Making 05, no. 04 (2006): 611–21. http://dx.doi.org/10.1142/s0219622006002155.

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The paper is devoted to a visualization-based method for exploration of relational databases that contain large volumes of uncertain data. The visualization is aimed at exploration of properties of the data and selecting a small number of interesting items from the database. The method introduced here is a new development of the Reasonable Goals method, which has already been implemented on Internet in the form of the Web application server. Thus, the new method can be applied on Internet, too. It can be used for selection-aimed data mining in various fields including environmental planning, m

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Abdulkadium, Ahmed Mahdi, Raid Abd Alreda Shekan, and Haitham Ali Hussain. "Application of Data Mining and Knowledge Discovery in Medical Databases." Webology 19, no. 1 (2022): 4912–24. http://dx.doi.org/10.14704/web/v19i1/web19329.

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While technical improvements in the form of computer-based healthcare information applications as well as hardware are enabling collecting of and access to healthcare data wieldier. In this context, there are tools to analyse and examine this medical data once it has been acquired and saved. Analysis of documented medical data records may help in the identification of hidden features and patterns that could significantly increase our understanding of disease onset and treatment therapies. Significantly, the progress in information and communications technologies (ICT) has outpaced our capacity

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Reynaldo, Jason, and David Boy Tonara. "Data Mining Application using Association Rule Mining ECLAT Algorithm Based on SPMF." MATEC Web of Conferences 164 (2018): 01019. http://dx.doi.org/10.1051/matecconf/201816401019.

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Data mining is an important research domain that currently focused on knowledge discovery database. Where data from the database are mined so that information can be generated and used effectively and efficiently by humans. Mining can be applied to the market analysis. Association Rule Mining (ARM) has become the core of data mining. The search space is exponential in the number of database attributes and with millions of database objects the problem of I/O minimization becomes paramount. To get the information and the data such as, observation of the master data storage systems and interviews

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Wu, You, Zheng Wang, and Shengqi Wang. "Human Resource Allocation Based on Fuzzy Data Mining Algorithm." Complexity 2021 (June 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/9489114.

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Data mining is currently a frontier research topic in the field of information and database technology. It is recognized as one of the most promising key technologies. Data mining involves multiple technologies, such as mathematical statistics, fuzzy theory, neural networks, and artificial intelligence, with relatively high technical content. The realization is also difficult. In this article, we have studied the basic concepts, processes, and algorithms of association rule mining technology. Aiming at large-scale database applications, in order to improve the efficiency of data mining, we pro

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Mohamad Hassin Azeez, Naofal. "Data Mining Approach for Predicting Learner's Achievement." University of Thi-Qar Journal of Science 6, no. 2 (2017): 104–12. http://dx.doi.org/10.32792/utq/utjsci/v6i2.23.

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Student achievement variables that may be included into student database can be classified into three main categories, student variables. Instructor variables and general variables. This paper presents a new machine-learning model for extracting knowledge From student attributes in a given database. This knowledge can be used for determining the relative importance and effectiveness of student's attributes for the prediction of their college academic achievement, and the relationship between these attributes and their achievement. The model includes three main algorithms namely: preprocessing

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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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Selvamani, D., and V. Selvi. "Association Rule Mining for Intrusion Detection System: A Survey." Asian Journal of Engineering and Applied Technology 8, no. 1 (2019): 20–24. http://dx.doi.org/10.51983/ajeat-2019.8.1.1065.

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Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundre

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Nguyen, Linh Hoai Thi. "Using two threshold for mining erasable itemset on dynamic incremental database." Journal of Development and Integration, no. 72 (August 31, 2023): 61–66. http://dx.doi.org/10.61602/jdi.2023.72.08.

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Traditional data mining is often applied in static databases and bath processing. In fact, databases are often changed, bath processing is not suitable because it consumes more time to mine in the whole database. Therefore, mining in dynamic databases attracted many researchers where mining erasable itemsets (EIs) in incremental databases is one of interesting areas. Recent years, there are some publications developed for updated EIs in dynamic databases but they consume more time to rescan the original database. In this paper we propose an algorithm for updating EIs using two minimum threshol

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Shepherd, R., S. A. Forbes, D. Beare, et al. "Data mining using the Catalogue of Somatic Mutations in Cancer BioMart." Database 2011 (May 23, 2011): bar018. http://dx.doi.org/10.1093/database/bar018.

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Aye, Pwint Phyu, and Khaing Wai Khaing. "To Development Manufacturing and Education using Data Mining A Review." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2168–73. https://doi.org/10.5281/zenodo.3591179.

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas. Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is the extraction of information from huge volume of data or set through the use of various data mining techniques. The data mining techniques like clustering, classification help in finding the hidden and previously unknown information from the database. In addition, data mining also important role and educational sector. Edu

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