Статті в журналах: "Hidden Data Mining"

1

Wang, Lidong, and Guanghui Wang. "Data Mining Applications in Big Data." Computer Engineering and Applications Journal 4, no. 3 (September 20, 2015): 143–52. http://dx.doi.org/10.18495/comengapp.v4i3.155.

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Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.

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Maryoosh, Amal Abdulbaqi, and Enas Mohammed Hussein. "A Review: Data Mining Techniques and Its Applications." International Journal of Computer Science and Mobile Applications 10, no. 3 (March 30, 2022): 1–14. http://dx.doi.org/10.47760/ijcsma.2022.v10i03.001.

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Data mining is a set of processes by which knowledge is extracted from huge amounts of data. Data mining is used to extract useful patterns and hidden information from this data. Machine learning techniques help in the comprehension of the hidden knowledge in the data. Data mining is considered an important field of research and is used in many different fields such as fraud detection, financial banking, education, healthcare, agriculture, industry, etc. In this paper, we will highlight some fundamentals of data mining and its applications. Also, we will conduct a comparative study among different reviews, combining literary studies that employed data mining techniques in various fields and reviewing the latest developments in this field.

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Sharma, Pragati, and Dr Sanjiv Sharma. "DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW." International Journal of Engineering Technologies and Management Research 5, no. 2 (May 1, 2020): 166–77. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.641.

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Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.

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4

Carpenter, Chris. "Data Mining of Hidden Danger in Operational Production." Journal of Petroleum Technology 71, no. 08 (August 1, 2019): 71–78. http://dx.doi.org/10.2118/0819-0071-jpt.

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5

Wang, Zhi Yan, Bei Zhan Wang, and Yi Dong Wang. "Data Mining Technology Applied in Network Security." Advanced Materials Research 989-994 (July 2014): 4974–79. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4974.

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As the Internet is developing fast, network security issues are rising. Data mining technology is applied to the analysis and understanding data, revealing hidden data secret inside knowledge. Especially high dimensional data mining can be used in information security data analyze, making the study of high-dimensional data mining very important. In this paper, traditional data mining is introduced; the concept and core ideas of high dimensional data mining are described, as well as its applications in network security.

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Liu, Jing, Qing Xiang Zhu, Xin Yu, Jing Xin Wang, and Yi Ge Huang. "The Research of Warning Model of Hidden Failure Based on Data Mining." Key Engineering Materials 693 (May 2016): 1844–48. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1844.

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Complex equipment is mainly used in important areas of national defense, health care, banking, etc. Consequences of failure are relatively severe, while the hidden failures are contained in the most complex devices as the process is running. Hidden failures in the normal operation of the device is difficult to find, and only under certain conditions will be triggered, while other faults may be led. The stability of the running system will be undermined. In order to monitor the occurrence and development of hidden failure of complex equipment, a hidden failure warning model based on data mining has been put forward, and the theory of the model has been analyzed, the selection gist of the model parameters has been given. The result shows that the accuracy of hidden failure impact value forecast by the model is 93.33%, the impact degree of the hidden failure effect on the dominant failure can be effectively monitored, and the model makes a good preventative effect against the sudden failure caused by the hidden failure.

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Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Migrating From Data Mining to Big Data Mining." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.

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Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples. Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.

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8

Tang, Yu, and Guo Hui Li. "Data Mining and Visualization System Design and Development." Advanced Materials Research 971-973 (June 2014): 1444–48. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1444.

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With the rise of the network,everyday the video websites update plenty of video datas.Faced with a lot of video datas,if you only rely on the human to analyze the video datas in order to dig out the information hidden in the video room,it will take a lot of time and is difficult to achieve the desired result. This paper develops a data mining and visualization system,which visualized shows the relationship between the video datas through a network graph of nodes.Based on visualized showing the relationship between the video datas,the system provides the tool to analyze the video datas and dig out the information hidden in the video room.

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9

Chunfeng Liu, Shanshan Kong, Li Feng, and Yuqian Kang. "Outer P-sets and F- mining of Hidden Data." International Journal of Advancements in Computing Technology 4, no. 17 (September 30, 2012): 180–87. http://dx.doi.org/10.4156/ijact.vol4.issue17.21.

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10

Gozali, Elahe, Bahlol Rahimi, Malihe Sadeghi, and Reza Safdari. "Diagnosis of diseases using data mining." Medical Technologies Journal 1, no. 4 (November 29, 2017): 120–21. http://dx.doi.org/10.26415/2572-004x-vol1iss4p120-121.

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Introduction: In the information age, data are the most important asset for health organizations. In the case of using data in useful and optimal manner, they can become financial resources for organization. Data mining is an appropriate method to transform this potential value into strategic information. Data mining means extraction of hidden information, recognition of hidden relationships and patterns, and in general, discovery of useful knowledge at high volume. The objective of this review paper was to evaluate using data mining in diagnoses of diseases. Methods: This research is a review paper conducted based on a structured review of the papers published in Science Direct, Pubmed, Google Scholar, SID, Magiran (between years 2005 and 2015) and books related to using data mining in medical science and using it in diagnose of diseases with related keywords. Results: Nowadays, data mining is used in many medical science studies, including diagnosis of diseases, discovering the hidden patterns in data, and so on. New ideas such as discovery of Knowledge from Discovery and Data Mining Database, which includes data mining techniques, have found more popularity and they has becomedesired research tool for researchers. Researchers can use them to identify patterns and relationshipsamong great number of variables. Using them, researchers have been able to predict theresults obtained from one disease by using information stores available in databases. Several studies have indicated that data mining is used widely in diagnosis of diseases based on types of information (medical images, characteristics of patients, and so on), such as tuberculosis, types of cancers, infectious diseases, and diagnosis of anomalies rarely diagnosed by human (spots and particular points within aye, which is the symptom of onset of blindness resulting from diabetes), determining type of behavior with patients, and predicting the success rate of surgical surgeries, determining the success rate of therapeutic methods in coping with incurable diseases, and so on. Conclusion: One of the most important challenging topics in healthcare is transformation of raw clinical data into meaningful information following continuous generation of great number of data. In current competitive environment, health organizations using technologies such as data mining to improve healthcare quality will achieve success faster. Many of research centers in Iran are faced with large volume of information, which is not analyzed at all or will be time-consuming due to using traditional methods, even in the case of using analysis and converting them to knowledge. In light of using data mining and its implementation, health organizations can transform the data into a powerful and competitive tool and take new steps in preventing, diagnosing, treating, and providing high-quality services for clients.

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11

Marchuk, G. V., V. L. Levkivskyy, and S. S. Kaliberda. "INTELLECTUAL ANALYSIS OF DATA." Bionics of Intelligence 1, no. 92 (June 2, 2019): 65–70. http://dx.doi.org/10.30837/bi.2019.1(92).11.

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The main research of the article is the data mining methods, such as linear and polynomial regression and the support vector machine. The application success is based on the fact that the methods and technologies of Data mining ensure the study of data and the research of hidden patterns in them. The analysis assists in identification of various features and data parameters, and therefore it is a powerful tool in the stage of forming forecasting models.

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12

Baby, T., and Dr P. Nirmaladevi. "A SURVEY ON DETECTION METHODS USING DATA MINING." International Journal of Engineering Applied Sciences and Technology 7, no. 2 (June 1, 2022): 252–57. http://dx.doi.org/10.33564/ijeast.2022.v07i02.039.

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Extraction of information from huge quantities of data is known as data mining. In other words, data mining is the process of mining knowledge from data. It is a multi-disciplinary skill to extract information and evaluate future events probability. The recent applications of Data Mining are marketing, fraud detection, scientific discovery, etc. All of these will detect previously unknown although acceptable relationships among the data which are already hidden, unsuspected, and previously unknown. Nowadays, it is mainly used in information security. The significance of data mining in malware detection, intrusion detection, and fraud detection are explored in this research study

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13

Singh, Pushpa, and Narendra Singh. "Role of Data Mining Techniques in Bioinformatics." International Journal of Applied Research in Bioinformatics 11, no. 1 (January 2021): 51–60. http://dx.doi.org/10.4018/ijarb.2021010106.

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Data mining offers a highly effective technique that is useful in research and development of bioinformatics. Bioinformatics consists biological information such as DNA, RNA, and protein. Data mining tasks/techniques are classification, prediction, clustering, association, outlier detection, regression, and pattern tracking. Data mining provides important correlation, hidden patterns, and knowledge from the bioinformatics data set. This paper presents the role of data mining techniques in bioinformatics application. Classification of gene and protein structure, analyzing the gene expression, association of co-disease, outlier detection and gene selection, protein structure prediction, and drug discovery are some typical biological example that has proven data mining as a suitable technique for bioinformatics.

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Zia, Amjad, Muzzamil Aziz, Ioana Popa, Sabih Ahmed Khan, Amirreza Fazely Hamedani, and Abdul R. Asif. "Artificial Intelligence-Based Medical Data Mining." Journal of Personalized Medicine 12, no. 9 (August 24, 2022): 1359. http://dx.doi.org/10.3390/jpm12091359.

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Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.

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Masih, Nancy, and Sachin Ahuja. "Prediction of Heart Diseases Using Data Mining Techniques." International Journal of Big Data and Analytics in Healthcare 3, no. 2 (July 2018): 1–9. http://dx.doi.org/10.4018/ijbdah.2018070101.

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Health care organizations accumulate large amount of healthcare data, but it is not ‘extracted' to draw hidden patterns which can prove efficient for the decision making process. Data mining techniques can be used to gain insights by discovering hidden patterns which remain undetected manually. Data analytics proves to be useful in detection and identification of the diseases. A complete analysis has been conducted on the FHS (Framingham Heart Study) using various data analytic techniques viz. Decision tree, Naïve Bayes, Support vector machine (SVM) and Artificial neural network (ANN) and the results were ranked according to the accuracy. ANN produce better results than other classification algorithms. The output helps to find out the prominent features that cause heart disease and also identifies the most common features that must be analyzed for prediction of deaths due to heart disease. Despite various studies carried out on heart diseases, the main focus of this study is prediction of heart disease on the dataset of FHS by using various classification algorithms to achieve high accuracy.

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Job, Minimol Anil. "Data Mining Techniques Applying on Educational Dataset to Evaluate Learner Performance Using Cluster Analysis." European Journal of Engineering Research and Science 3, no. 11 (November 21, 2018): 25–31. http://dx.doi.org/10.24018/ejers.2018.3.11.966.

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Due to the advancement of technology in this digital era, academic institutions are bringing out graduates as well as generating enormous amounts of data from their systems. Hidden information and hidden patterns in large datasets can be efficiently analyzed with data mining techniques. Application of data mining techniques improves the performance of many organizational domains and the concept can be applied in the education sectors for their performance evaluation and improvement. Understanding the business value of the collected data it can be used for classifying and predicting the students’ behavior, academic performance, dropout rates, and monitoring progression and retention. This paper discusses how application of data mining can help the higher education institutions by enabling better understanding of the student data and focuses to consolidate clustering algorithms as applied in the context of educational data mining.

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Job, Minimol Anil. "Data Mining Techniques Applying on Educational Dataset to Evaluate Learner Performance Using Cluster Analysis." European Journal of Engineering and Technology Research 3, no. 11 (November 21, 2018): 25–31. http://dx.doi.org/10.24018/ejeng.2018.3.11.966.

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Due to the advancement of technology in this digital era, academic institutions are bringing out graduates as well as generating enormous amounts of data from their systems. Hidden information and hidden patterns in large datasets can be efficiently analyzed with data mining techniques. Application of data mining techniques improves the performance of many organizational domains and the concept can be applied in the education sectors for their performance evaluation and improvement. Understanding the business value of the collected data it can be used for classifying and predicting the students’ behavior, academic performance, dropout rates, and monitoring progression and retention. This paper discusses how application of data mining can help the higher education institutions by enabling better understanding of the student data and focuses to consolidate clustering algorithms as applied in the context of educational data mining.

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18

Ahmad, Andi Abdul malik, Zawiyah Saharuna, and Muhammad Fajri Raharjo. "Pemanfaatan Data Mining dalam Penentuan Rekomendasi Mustahik (Penerima Zakat)." Elektron : Jurnal Ilmiah 12, no. 2 (December 2, 2020): 67–73. http://dx.doi.org/10.30630/eji.12.2.182.

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This study applies data mining in determining recommendations for mustahik. The application is carried out using a classification method with an artificial neural network algorithm where the attributes used are age and type of work of the head of the family, the condition and ownership of the residence, the place of sewage, family monthly income, number of dependents, and diet. Tests are carried out using a combination of values between learning rate, epoch, k-fold, and hidden layer neurons. Based on the test results from the classification process, it is found that the artificial neural network algorithm has the highest accuracy when the number of hidden layer neurons is six, the learning rate is one, the fold is seven, and the number of epochs is 200, which is 92.09%. The test results are then displayed on the Mustahik information system page.

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Ahmad, Andi Abdul malik, Zawiyah Saharuna, and Muhammad Fajri Raharjo. "Pemanfaatan Data Mining dalam Penentuan Rekomendasi Mustahik (Penerima Zakat)." Elektron : Jurnal Ilmiah 12, no. 2 (December 2, 2020): 67–73. http://dx.doi.org/10.30630/eji.12.2.182.

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This study applies data mining in determining recommendations for mustahik. The application is carried out using a classification method with an artificial neural network algorithm where the attributes used are age and type of work of the head of the family, the condition and ownership of the residence, the place of sewage, family monthly income, number of dependents, and diet. Tests are carried out using a combination of values between learning rate, epoch, k-fold, and hidden layer neurons. Based on the test results from the classification process, it is found that the artificial neural network algorithm has the highest accuracy when the number of hidden layer neurons is six, the learning rate is one, the fold is seven, and the number of epochs is 200, which is 92.09%. The test results are then displayed on the Mustahik information system page.

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Xi, Haixu, Feiyue Ye, Sheng He, Yijun Liu, and Hongfen Jiang. "Bayes Performance of Batch Data Mining Based on Functional Dependencies." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 03 (February 19, 2019): 1959011. http://dx.doi.org/10.1142/s0218001419590110.

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Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

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21

Zhu, Jian Xin. "Arithmetic Research on Data Mining Technology and Associative Rules Mining." Applied Mechanics and Materials 556-562 (May 2014): 3949–51. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3949.

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Data mining is a technique that aims to analyze and understand large source data reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. Why there have been more attentions to it from researchers or businessmen is due to the wide availability of huge amounts of data and imminent needs for turning such data into valuable information. During the past decade or over, the concepts and techniques on data mining have been presented, and some of them have been discussed in higher levels for the last few years. Data mining involves an integration of techniques from database, artificial intelligence, machine learning, statistics, knowledge engineering, object-oriented method, information retrieval, high-performance computing and visualization. Essentially, data mining is high-level analysis technology and it has a strong purpose for business profiting. Unlike OLTP applications, data mining should provide in-depth data analysis and the supports for business decisions.

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Abdullah Qaid Aqlan, Ameen. "Data Mining and Revealing Hidden Sentiment in Tweets Using Spark." International Journal on Data Science and Technology 8, no. 1 (2022): 14. http://dx.doi.org/10.11648/j.ijdst.20220801.13.

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23

Rahman, Nayem. "Data Mining Problems Classification and Techniques." International Journal of Big Data and Analytics in Healthcare 3, no. 1 (January 2018): 38–57. http://dx.doi.org/10.4018/ijbdah.2018010104.

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Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

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Acar, Ece, Görkem Sarıyer, Vipul Jain, and Bharti Ramtiyal. "Discovering Hidden Associations among Environmental Disclosure Themes Using Data Mining Approaches." Sustainability 15, no. 14 (July 22, 2023): 11406. http://dx.doi.org/10.3390/su151411406.

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Environmental concerns play a crucial role in sustainability and public opinion on supply chains. This is why, how, and to what extent the firms experience environmental-related actions and inform their stakeholders, which is under discussion by most researchers. This paper aims to leverage data mining and its capabilities by applying association rule mining to the environmental disclosure context. With the aim of extracting hidden relationships between environmental disclosure themes for BIST 100 firms serving the Turkish supply chain, this research implements a novel association rule mining approach and uses the Apriori algorithm. With this purpose, the environmental information of BIST 100 firms was collected manually from sustainability reports; the raw data were processed; and the following seven themes identified the representing firms’ disclosure items: environmental management, climate change, energy management, emissions management, water management, waste management, and biodiversity management. The results indicate various hidden relations between the sector and disclosures, allowing us to generate sector-based rules between environmental disclosure themes.

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Zhao, Hong Yan. "Decision Tree Technology in Data Classification." Applied Mechanics and Materials 268-270 (December 2012): 1752–57. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1752.

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With the development of database technology as well as the widespread application of database management system, the capability of collecting data was improved rapidly, and lots of data have been accumulated. Data mining was created for the purpose of excavate the useful knowledge hidden behind these data. Data classification is not only an important issue of data mining but also an effective KDD method. Decision Tree, which is a major technology of data classification, is applied far and widely. In this article, the concrete step of mining data by decision tree, the main algorithm and the basic idea of decision tree were summarized and analysed.

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Kommineni, Madhuri, Someswari Perla, and Divya Bharathi Yedla. "A Survey of using Data Mining Techniques for Soil Fertility." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 917. http://dx.doi.org/10.14419/ijet.v7i2.7.11096.

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Data Mining is a technique which focuses on large data sets to extract information for prediction and discovery of hidden patterns. Data Mining is applicable on various areas like healthcare, insurance, marketing, retail, communication, agriculture. Agriculture is the backbone of country’s economy. It is the important source of livelihood. Agriculture mainly depends on climate, topography, soil, biology. Agricultural Mining is a technology which can bring knowledge to agriculture development. Data Mining in agriculture plays a role in weather forecasting, yield prediction, soil fertility, fertilizers usage, fruit grading, plant disease and weed detection. The current study presents the different data mining techniques and their role in context of soil fertility, nutrient analysis.

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Rajalakshmi, M., T. Purusothaman, and S. Pratheeba. "Collusion-Free Privacy Preserving Data Mining." International Journal of Intelligent Information Technologies 6, no. 4 (October 2010): 30–45. http://dx.doi.org/10.4018/jiit.2010100103.

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Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.

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ALI, Sura I. Mohammed, and Rafid Habib BUTI. "DATA MINING IN HEALTHCARE SECTOR." MINAR International Journal of Applied Sciences and Technology 03, no. 02 (June 1, 2021): 87–91. http://dx.doi.org/10.47832/2717-8234.2-3.11.

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Disease detection is one of the applications where data mining techniques achieved more accurate and useful results. The healthcare sector collects massive volumes of healthcare data that are not mine to discover hidden data for better decision-making, a field of data mining introduces more efficiently and effectively to predict different kinds of diseases. Clustering medical data into small, meaningful chunks will help in pattern discovery by allowing for the retrieval of a large number of specific data points. The difference in using clustering the medical data from traditional data mining techniques is in extracting many features of the dataset that have been split into small segments to enable us to discover patterns by adding the data structure. By using clustering techniques, discovered overall correlations between data attributes. Selected data processing makes the mining process more efficient. The processed disease data are clustered using the K-means algorithm with the K values. Its ease of use and speed, which enable it to perform on a massive dataset. This paper highlights the theoretical side in using the K-Means Clustering algorithm in the context of data mining of disease detection and allowing for reliable and effective diagnosis.

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Kozlova, Anastasia, Vladislav Kukartsev, Vladimir Melnikov, Georgiy Kovalev, and Alexander Stashkevich. "Finding dependencies in the corporate environment using data mining." E3S Web of Conferences 431 (2023): 05032. http://dx.doi.org/10.1051/e3sconf/202343105032.

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The article analyses the influence of factors of the work environment, as well as the non-work environment, on the employee's departure from the company. A dataset containing 1470 data rows with 14 attributes belonging to the company's employees was selected for the analysis. The method of self-organising Kohonen maps was used, which allow to study the structure of the data and identify hidden patterns, as well as the method of artificial neural networks, which allow to analyse large amounts of data and find hidden relationships that may not be obvious to humans. In the course of the work, the errors of the methods were determined, several experiments with different number of factors were conducted, and the dependence between the number of factors and the magnitude of the error of the algorithms was revealed. For both methods and each experiment, conjugacy tables were obtained, which contain the classification results obtained by the methods. In addition, a correlation analysis was performed to determine the degree of association between the factors and the target variable.

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Bilal Zorić, Alisa. "Benefits of Educational Data Mining." Journal of International Business Research and Marketing 6, no. 1 (2020): 12–16. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.61.3002.

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We live in a world where we collect huge amounts of data, but if this data is not further analyzed, it remains only huge amounts of data. With new methods and techniques, we can use this data, analyze it and get a great advantage. The perfect method for this is data mining. Data mining is the process of extracting hidden and useful information and patterns from large data sets. Its application in various areas such as finance, telecommunications, healthcare, sales marketing, banking, etc. is already well known. In this paper, we want to introduce special use of data mining in education, called educational data mining. Educational Data Mining (EDM) is an interdisciplinary research area created as the application of data mining in the educational field. It uses different methods and techniques from machine learning, statistics, data mining and data analysis, to analyze data collected during teaching and learning. Educational Data Mining is the process of raw data transformation from large educational databases to useful and meaningful information which can be used for a better understanding of students and their learning conditions, improving teaching support as well as for decision making in educational systems.The goal of this paper is to introduce educational data mining and to present its application and benefits.

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Et. al., Divvela Srinivasa Rao,. "A SURVEY ON FREQUENT ITEM SET MINING FOR LARGE TRANSACTIONAL DATA." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (April 10, 2021): 885–93. http://dx.doi.org/10.17762/itii.v9i2.426.

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In the decision making process the Data Analytics plays an important role. The Insights that are obtained from pattern analysis gives many benefits like cost cutting, good revenue, and better competitive advantage. On the other hand the patterns of frequent itemsets that are hidden consume more time for extraction when data increases over time. However less memory consumption is required for mining the patterns of frequent itemsets because of heavy computation. Therefore, an algorithm required must be efficient for mining the patterns of the frequent itemsets that are hidden which takes less memory with short run time. This paper presents a review of different algorithms for finding Frequent Patterns so that a more efficient algorithm for finding frequent items sets can be developed.

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Hassani, Hossein, Christina Beneki, Stephan Unger, Maedeh Taj Mazinani, and Mohammad Reza Yeganegi. "Text Mining in Big Data Analytics." Big Data and Cognitive Computing 4, no. 1 (January 16, 2020): 1. http://dx.doi.org/10.3390/bdcc4010001.

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Rony, Md Sumon, Sagor Chandra Bakchy, and Hadisur Rahman. "Crime Detection using Data Mining Techniques." Computer Science & Engineering: An International Journal 10, no. 5 (October 30, 2020): 1–5. http://dx.doi.org/10.5121/cseij.2020.10501.

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As crime rates keep spiraling each day, new challenges are faced by law enforcement agencies. They have to keep their on the lookout for any signs criminal activity. The law enforcement agencies should therefore be able to predict such increase or decrees or trends in crime. Such as theft, Killing. Crime that may occur in a particular area in a particular month, year, any timespan. Data mining is defined as a process of discovering hidden valuable knowledge by analyzing large amounts of data, which is stored in databases or data warehouse, using various data mining techniques such as machine learning, artificial intelligence, statistical. Many algorithms for data mining approach to help detect the crimes patterns. Data Collection, Data Preprocessing Phase, Data Filtering, Linier Regression. Wekasoft are used for collection of data analyzing. Visualization finally get results. The advantage of using this tool is that clustering will be performed automatically.

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Sun, Shuya, and Qingsheng Li. "A Behavior Change Mining Method Based on Complete Logs with Hidden Transitions and Their Applications in Disaster Chain Risk Analysis." Sustainability 15, no. 2 (January 14, 2023): 1655. http://dx.doi.org/10.3390/su15021655.

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The aim of change mining is to discover changes in process models based on execution data recorded in event logs. There may be hidden transitions in the process models related to, for example, business integration and user requirements that do not exist in event logs. Behavioral change mining in the case of hidden transitions is a fundamental problem in the field of change mining. Existing research on change mining has not considered the effects of hidden transitions. This paper proposes a novel method based on complete logs with hidden transitions for mining behavioral changes. We analyze the behavioral relations of activities based on changed logs under the condition that the original model is unknown. Log-driven change mining is realized by calculating the log behavioral profile, minimum successor relation, and log-weighted coefficient, which allows the mining of hidden transitions, as well as changed behavioral relations. Finally, this method is applied to disaster chain risk analysis, and the evolution of disaster chains in different scenarios is mined from disaster logs to determine the type of disaster chain. The results of this paper provide a scientific basis for the strategy of chain-cutting disaster mitigation in the emergency management of disaster chains.

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Mohan, S. Vijayarani, and Tamilarasi Angamuthu. "Association Rule Hiding in Privacy Preserving Data Mining." International Journal of Information Security and Privacy 12, no. 3 (July 2018): 141–63. http://dx.doi.org/10.4018/ijisp.2018070108.

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This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing information about a business. Significant association rules provide knowledge to the data miner as they effectively summarize the data, while uncovering any hidden relations among items that hold in the data. Association rule hiding techniques are used for protecting the knowledge extracted by the sensitive association rules during the process of association rule mining. Association rule hiding refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non-sensitive rules. In this article, two new hiding techniques are proposed namely hiding technique based on genetic algorithm (HGA) and dummy items creation (DIC) technique. Hiding technique based on genetic algorithm is used for hiding sensitive association rules and the dummy items creation technique hides the sensitive rules as well as it creates dummy items for the modified sensitive items. Experimental results show the performance of the proposed techniques.

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RenugaDevi, R., and M. Hemalatha. "A Novel Approach for Secure Hidden Community Mining in Social Networks using Data Mining Techniques." International Journal of Computer Applications 87, no. 7 (February 14, 2014): 12–19. http://dx.doi.org/10.5120/15219-3728.

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Chandra Jena, Prakash, Subhendu Kumar Pani, and Debahuti Mishra. "A novel approach to ensemble learning in distributed data mining." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 233. http://dx.doi.org/10.14419/ijet.v7i2.33.14159.

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Several data mining techniques have been proposed to take out hidden information from databases. Data mining and knowledge extraction becomes challenging when data is massive, distributed and heterogeneous. Classification is an extensively applied task in data mining for prediction. Huge numbers of machine learning techniques have been developed for the purpose. Ensemble learning merges multiple base classifiers to improve the performance of individual classification algorithms. In particular, ensemble learning plays a significant role in distributed data mining. So, study of ensemble learning is crucial in order to apply it in real-world data mining problems. We propose a technique to construct ensemble of classifiers and study its performance using popular learning techniques on a range of publicly available datasets from biomedical domain.

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Liu, Yizhou. "Application and practice of data mining techniques." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 237–41. http://dx.doi.org/10.54254/2755-2721/4/20230456.

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Data mining is a relatively recent phenomenon, but it has emerged as a result of the rapid advancement of information technology and the associated technologies and computer functions. In order to facilitate social production and life, advances in computer data mining, big data, and cloud storage technologies have been made. Data mining, or the process of systematically extracting useful information from large data sets, has many potential applications and can be applied in many different fields. Using a literature review method, this paper defines key terms associated with big data technology and demand, data mining technology, before outlining several widely used techniques for mining this type of information and expanding on the future directions of application in a number of different domains. Values hidden in data are becoming increasingly apparent in the modern world. The growing field of data mining offers significant help in finding a solution to this issue. Exploring the unique content and application practice of data mining technology in the era of big data may provide some reference for social development when taken together with the real situation.

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Khoshahval, S., M. Farnaghi, and M. Taleai. "SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 395–99. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-395-2017.

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Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.

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Wang, Yue, and Tian Jun Lu. "Application Analysis of Smart Sensor Node Based on Data Mining Association Technology." Advanced Materials Research 1078 (December 2014): 254–57. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.254.

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Smart sensor has the following three advantages: realize the information acquisition of high precision and low cost, it relates to the micro mechanical and microelectronic, signal processing and computer technology. The purpose of data mining is to discover knowledge. Knowledge of association rules mining aims to find out the related information hidden in the database. The paper presents application analysis of smart sensor node based on data mining association technology. Experimental results show the proposed methodology has advantages in the management of the intelligent node.

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Yu, Rong. "Research of the Tourism Marketing Basing on the Data Mining." Applied Mechanics and Materials 543-547 (March 2014): 3659–62. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3659.

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With the development of economy, the tourism industry has become a pillar industry for national development. To speed up the development of the tourism industry, more rational capital investment for tourism, the reasonable layout of the facilities, make scientific decisions will be very important to tourism. Data mining technology has had a profound impact in many industries and areas. Among data mining association rules mining, because it can find a lot of interesting connections between data items, can provide the basis for our decision-making. Therefore, we will introduce data mining into tourism industry. Find out the hidden link in the tourism industry, in order to provide the basis for scientific decision-making.

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Lin, Yun Feng, and Xiao Ping Hu. "The Application of Data Mining Technology in Mechanical Fault Diagnosis." Key Engineering Materials 460-461 (January 2011): 821–26. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.821.

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This article first introduced the survey of mechanical fault diagnosis technology development and the data mining technology theory. Then its application situation at present and the main questions that exist are elaborated. Its development trend is analyzed. The application feasibility of using data mining technology in mechanical fault diagnosis is discussed. Next, the naissance, the development and the future development tendency of data mining technology are introduced. The four algorithms are analyzed and the framework is built too. Intelligent Diagnosis is a major development direction of the fault diagnosis. Knowledge acquisition is the bottleneck of Intelligent Diagnosis development. It comprehensive use of many kinds of advanced technology, discover valuable and hidden knowledge from the large amounts of data mining.

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Wang, Jing, Qiuhong Zhang, and Meijing Guan. "Learning Behavior Based on Data Mining Technology." Security and Communication Networks 2022 (October 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/6155704.

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With the release of the Education Informatization 2.0 Action Plan and the rapid development of learning analysis technology, educational data mining becomes a new research direction. Data mining can improve teachers’ teaching methods and students’ learning skills by acquiring information hidden in the educational data. Based on the learning behavior data of college students, this paper uses BP neural network, a data mining method, to predict their comprehensive evaluation results. The results show that there is a close relationship between students’ learning behavior and their comprehensive scores. In addition, models of naive Bayes, logistic regression, and decision tree are established for verification and comparison. Compared with other models, BP neural network model has higher prediction accuracy and better performance. It can serve as an important basis to improve students’ learning methods and teachers’ teaching methods.

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Khodadadi, Nima, M. G. El El-Mahgoub, and Rokaia M. Zaki. "Mining Sematic Association Rules from RDF Data." Journal of Artificial Intelligence and Metaheuristics 4, no. 1 (2023): 43–51. http://dx.doi.org/10.54216/jaim.040105.

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Many fields rely heavily on the accurate and consistent portrayal of structured data. In order to effectively express and link information on the Semantic Web, RDF (Resource Description Framework) data is essential. Here, we present a process for extracting semantic association rules from RDF data. For our method, we employ the Apriori algorithm to mine the RDF triples for hidden connections between ideas and relationships. Using metrics such as confidence, support, and lift, we examine how well our model performs. We also give visual representations, like as scatter plots and clustered matrices, to make the correlations easier to understand and analyse. The findings validate our model's potential to unearth significant relationships, which in turn reveal important details about the RDF data's underlying semantics. Our findings are discussed, and suggestions for further study are provided.

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Bao, Ang, Wei Guo Pan, and Wen Huan Wang. "Advances in Data Mining and Applications in Power Plants." Advanced Materials Research 347-353 (October 2011): 487–93. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.487.

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Describes the theory and methods of data mining technology, and the latest research progress home and abroad. In the equipment operation of various thermal power plants, more and more field data is stored in the DCS real-time database, and there is always an abundance of knowledge hidden behind the data. Adopting the date mining technology to process and analyze these data can optimize the operation of power plants and provide effective means for monitoring and evaluation of the equipment.

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Bolnokin, V. E., D. I. Mutin, E. I. Mutina, V. G. Vyskub, and O. Ja Kravets. "Data mining technologies in a distributed medical information system." Journal of Physics: Conference Series 2094, no. 3 (November 1, 2021): 032005. http://dx.doi.org/10.1088/1742-6596/2094/3/032005.

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Abstract Proposed a method for solving the problem of identifying hidden relationships in hard-to-structure data that have an implicit character is considered using information mining. Proposed decision trees, the effectiveness of which is illustrated by a specific example. The use of OLAP analysis systems on data presented using in the form of a real or virtual hypercube’s information is an effective tool for the effectiveness of the management for medical monitoring

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Lei, QunBi. "Design of an Instant Data Analysis System for Sports Training Based on Data Mining Technology." International Journal of Web-Based Learning and Teaching Technologies 18, no. 2 (September 27, 2023): 1–15. http://dx.doi.org/10.4018/ijwltt.330991.

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Data mining (DM) is an in-depth approach to data analysis by mining useful information from large amounts of data, and this technique is now being used in an increasing number of fields. In this paper, the authors present the design of a real-time data analysis system for sports training based on DM technology and use the corresponding mining tools of DM technology to discover relevant patterns or laws hidden in the data. Therefore, using the real-time data analysis system for sports training based on DM technology, useful information and patterns for improving examination performance can be obtained, which can improve targeted teaching methods and help students overcome learning difficulties, providing rational teaching, synchronizing courses, establishing preparation, effectively guiding students in course selection, and improving course quality and educational effectiveness.

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Polyakov, Maxim, Igor Khanin, Gennadiy Shevchenko, and Vladimir Bilozubenko. "Data mining as a cognitive tool: Capabilities and limits." Knowledge and Performance Management 5, no. 1 (July 8, 2021): 1–13. http://dx.doi.org/10.21511/kpm.05(1).2021.01.

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Due to the large volumes of empirical digitized data, a critical challenge is to identify their hidden and unobvious patterns, enabling to gain new knowledge. To make efficient use of data mining (DM) methods, it is required to know its capabilities and limits of application as a cognitive tool. The paper aims to specify the capabilities and limits of DM methods within the methodology of scientific cognition. This will enhance the efficiency of these DM methods for experts in this field as well as for professionals in other fields who analyze empirical data. It was proposed to supplement the existing classification of cognitive levels by the level of empirical regularity (ER) or provisional hypothesis. If ER is generated using DM software algorithm, it can be called the man-machine hypothesis. Thereby, the place of DM in the classification of the levels of empirical cognition was determined. The paper drawn up the scheme illustrating the relationship between the cognitive levels, which supplements the well-known schemes of their classification, demonstrates maximum capabilities of DM methods, and also shows the possibility of a transition from practice to the scientific method through the generation of ER, and further from ER to hypotheses, and from hypotheses to the scientific method. In terms of the methodology of scientific cognition, the most critical fact was established – the limitation of any DM methods is the level of ER. As a result of applying any software developed based on DM methods, the level of cognition achieved represents the ER level.

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NAYAK, RICHI, and TIAN QIU. "A DATA MINING APPLICATION: ANALYSIS OF PROBLEMS OCCURRING DURING A SOFTWARE PROJECT DEVELOPMENT PROCESS." International Journal of Software Engineering and Knowledge Engineering 15, no. 04 (August 2005): 647–63. http://dx.doi.org/10.1142/s0218194005002476.

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Data mining techniques provide people with new power to research and manipulate the existing large volume of data. A data mining process discovers interesting information from the hidden data that can either be used for future prediction and/or intelligently summarising the details of the data. There are many achievements of applying data mining techniques to various areas such as marketing, medical, and financial, although few of them can be currently seen in software engineering domain. In this paper, a proposed data mining application in software engineering domain is explained and experimented. The empirical results demonstrate the capability of data mining techniques in software engineering domain and the potential benefits in applying data mining to this area.

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Jahanbakhsh, Maryam, Asal Aghadavodian Jolfaee, Roya Kelishadi, and Mohammad Sattari. "Extracting the Hidden Patterns Affecting Mental Health through Data Mining Techniques." Journal of Advances in Medical and Biomedical Research 30, no. 140 (May 1, 2022): 281–88. http://dx.doi.org/10.30699/jambs.30.140.281.

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