Relevant bibliographies by topics / History of data mining

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Academic literature on the topic 'History of data mining'

Author: Grafiati
Published: 5 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "History of data mining"

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Trivedi, Nripesh. "Data mining." International Journal of Scientific Research and Management (IJSRM) 12, no. 03 (2024): 1094. http://dx.doi.org/10.18535/ijsrm/v12i03.ec07.

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Data Mining Data mining is about finding patterns in the data [1]. In this paper, I put forward an important insight about similarity in branches of computer science and data mining. All branches of computer science could be termed as a procedure to carry out data mining. In this paper, I detail that. The computer works by finding patterns in the input and output [2]. Artificial Intelligence works by finding the patterns of functions of the related variables [3]. Machine learning works by mathematical justification of machine learning methods and results [4]. That is the pattern followed in machine learning. Social networking is about finding patterns in user behaviour and user engagement [5][6].
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Coenen, Frans. "Data mining: past, present and future." Knowledge Engineering Review 26, no. 1 (2011): 25–29. http://dx.doi.org/10.1017/s0269888910000378.

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AbstractData mining has become a well-established discipline within the domain of artificial intelligence (AI) and knowledge engineering (KE). It has its roots in machine learning and statistics, but encompasses other areas of computer science. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale data mining to be conducted. Unlike other innovations in AI and KE, data mining can be argued to be an application rather then a technology and thus can be expected to remain topical for the foreseeable future. This paper presents a brief review of the history of data mining, up to the present day, and some insights into future directions.
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Dr., P. Nithya*1 B. Uma Maheswari2 &. A. Nandini3. "A STUDY ON IMAGE MINING TECHNIQUES, FRAMEWORK AND APPLICATIONS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 611–15. https://doi.org/10.5281/zenodo.829795.

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Image mining refers to a data mining technique where images are used as data. It supports a large field of applications like medical diagnosis, agriculture, industrial work, space research and obviously the educational field. In this paper I would like to explain image mining- introduction, history, image mining process, image mining framework, application, techniques, various extraction mechanisms used in image mining and image retrieval based on semantics. Since image mining is now the most popular technique of retrieving information related to user query. These concepts are used to developing the research process and it gives the best image mining results.
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Dost, Muhammad Khan. "Data Streaming of Healthcare from Internet of Things (IoTs) using Big Data Analytics." Global Social Sciences Review 4, no. 1 (2019): 287–95. https://doi.org/10.5281/zenodo.4362047.

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The present study aims at the concept of the IoTs (IoT) and its relation with the healthcare sector. Nowadays, IoT is the main focus of researchers and scientists while this concept illustrates the data stream generated from IoT devices in massive amounts like big data with a continuous stream that requires its proper handling. This study aims at the analytical processing of big datasets having a medical history of patients and their diseases. The data cleansing is applied before going through the analytics phase due to the existence of some noisy and missing data. The analytics of data identified that what events are happening while the mining approaches identified why and how events are happening. Together, both phases help in data analytics and mining. Finally, the analytics and visualization led to the decision making and its results depict the effectiveness and efficiency of the proposed framework for data analytics in IoT
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Saiz-Gonzalez, D., E. Baca-García, M. Perez-Rodriguez, et al. "Searching for Variables Associated with Familial Suicide Attempts Using Data Mining Techniques." European Psychiatry 24, S1 (2009): 1. http://dx.doi.org/10.1016/s0924-9338(09)70963-0.

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Introduction:Adoption, twin and family studies suggest that suicide behavior is familial and heritable. Both completed and attempted suicide appear to be transmitted in a familial form. Genetics and environment influences had been detected in various studies. But suicidal behavior suggests to be inherited independently from the mental disorders usually associated with it. While traditional statistics emphasizes inference and estimations, data mining emphasizes the fulfillment of a task such as classification, estimation, or knowledge discovery.Objectives:The goal of this study was to determine in a large sample of suicide attempts which variables are associated with family history of attempted suicide.Methods:In an emergency room, 539 adult suicide attempters were recruited. The two dichotomous dependent variables were family history of suicide attempt (10%) and of completed suicide (4%). Independent variables were 101 clinical variables explored with two data mining techniques: Random Forest and Forward Selection.Results:A model for family history of completed suicide could not be developed. A classificatory model for family history of attempted suicide included the use of alcohol in the intent and family history of completed suicide, provide a sensitivity of 78.4%, a specificity of 98.7% and accuracy of 96.6%.Conclusions:A classificatory model for family history of completed suicide could not be developed using data mining techniques. But it suggested that the use of alcohol in the intent and family history of completed suicide may be associated with familial attempted suicide.
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Khanal, Rajesh. "The Role of Open Standard Electronic Health Record in Medical Data Mining." European Journal of Business Management and Research 2, no. 2 (2017): 1–7. http://dx.doi.org/10.24018/ejbmr.2017.2.2.9.

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Electronic Health Record (EHR) has received significant attention of all the health service provider in the world. EHR contains electronic information of all the patient information such as demographics, medical history, family medical history, lab tests and results, and prescribed drug. There is not any consistency in type of the EHR software implemented by the hosting organization. So, the EHR is currently vendor dependent and is not transferrable to another health service provider. The open standard electronic health record makes it public available to both vendor and patient. It can further aid in creating a universal EHR database for medical data mining. Mining the EHR helps in developing the best standard of care and clinical practice. The following paper proposes a universal EHR database and medical data mining. The benefits and challenges of implementing a database system is also discussed in the paper. The following paper will also analyze the different application areas of the EHR data mining.
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Anshari, Said Fadlan, and Sujacka Retno. "Penerapan Metode Nine-Step Kimball Dalam Pengolahan Data History Menggunakan Data Warehouse dan Business Intelligence." Jurnal Ilmu Komputer 16, no. 1 (2023): 69. http://dx.doi.org/10.24843/jik.2023.v16.i01.p07.

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Data warehouse dan business intelligence merupakan perpaduan teknologi informasi yang dapat dimanfaatkan oleh banyak perusahaan yang memiliki data histori dan data transaksi yang cukup besar untuk bisa selanjutnya diolah, seperti yang dimiliki oleh beberapa perusahaan waralaba. Dengan menggunakan metode nine-step Kimball sebagai metode pengembangan data warehouse-nya, seta aplikasi Tableau sebagai media visuailsasi dari hasil business intelligence-nya, perusahaan dapat melihat hasil pengolahan data histori dan data transaksi yang telah dihasilkan, yang berkaitan dengan fungsi bisnis penjualan atau transaksi. Hasil pengolahan data ini dapat dimanfaatkan oleh lintas tim dan seluruh stakeholder perusahaan untuk bisa melihat gambaran bisnis yang telah dijalankan, serta membantu untuk mendukung keputusan dalam pengembangan bisnis di waktu yang akan datang. Selain daripada itu, hasil pengolahan data dalam data warehouse ini dapat dimanfaatkan oleh perusahaan untuk diolah lebih lanjut, salah satunya menggunakan metode data mining, yang dapat dimanfaatkan untuk kebutuhan prediksi di masa depan.
 Kata kunci: data warehouse, business intelligence, nine-step Kimball, Tableau, data mining
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Splichal, Slavko. "In data we (don't) trust: The public adrift in data-driven public opinion models." Big Data & Society 9, no. 1 (2022): 205395172210973. http://dx.doi.org/10.1177/20539517221097319.

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This article seeks to address current debates comparing polls and opinion mining as empirically based figuration models of public opinion in the light of in-depth intellectual debates on the role and nature of public opinion that began after the French Revolution and the controversy over public opinion spurred by the invention of polls. Issues of historical quantification and re-conceptualisation of public opinion are addressed in four parts. The first summarises the history of the rise and fall of the concept of public opinion. The second re-examines the key controversies in the debates on the theoretical, empirical and social implications and consequences of the invention of polling. The third part scrutinises the datafication of public opinion that started with polling industry and continues in the age of big data and data mining. The final section discusses the controversial potentials of opinion-mining technology and suggests ways in which social scientists could critically respond to the big data and opinion-mining challenges in order to reintegrate the ideas of publicness, the public and public sphere into public opinion research.
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Bindzarova Gergelova, Marcela, Slavomir Labant, Jozef Mizak, Pavel Sustek, and Lubomir Leicher. "Inventory of Locations of Old Mining Works Using LiDAR Data: A Case Study in Slovakia." Sustainability 13, no. 12 (2021): 6981. http://dx.doi.org/10.3390/su13126981.

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The concept of further sustainable development in the area of administration of the register of old mining works and recent mining works in Slovakia requires precise determination of the locations of the objects that constitute it. The objects in this register have their uniqueness linked with the history of mining in Slovakia. The state of positional accuracy in the registration of objects in its current form is unsatisfactory. Different database sources containing the locations of the old mining works are insufficient and show significant locational deviations. For this reason, it is necessary to precisely locate old mining works using modern measuring technologies. The most effective approach to solving this problem is the use of LiDAR data, which at the same time allow determining the position and above-ground shape of old mining works. Two localities with significant mining history were selected for this case study. Positional deviations in the location of old mining works among the selected data were determined from the register of old mining works in Slovakia, global navigation satellite system (GNSS) measurements, multidirectional hill-shading using LiDAR, and accessible data from the open street map. To compare the positions of identical old mining works from the selected database sources, we established differences in the coordinates (ΔX, ΔY) and calculated the positional deviations of the same objects. The average positional deviation in the total count of nineteen objects comparing documents, LiDAR data, and the register was 33.6 m. Comparing the locations of twelve old mining works between the LiDAR data and the open street map, the average positional deviation was 16.3 m. Between the data sources from GNSS and the registry of old mining works, the average positional deviation of four selected objects was 39.17 m.
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Kumar, Ram, Tannya Gupta, Yamini Meshram, and Juhi Kumari. "Early Prediction of Cardiac Arrest Using Data Mining Algorithms." International Research Journal of Computer Science 10, no. 05 (2023): 140–49. http://dx.doi.org/10.26562/irjcs.2023.v1005.07.

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Cardiac arrest is a sudden and unexpected loss of heart function that can lead to death. Early prediction of cardiac arrest could help to improve survival rates by allowing for early intervention. Datamining is a field of computer science that involves the extraction of knowledge from large datasets. Data mining algorithms can be used to identify patterns in data that may be indicative of cardiac arrest. For example, data mining algorithms can be used to identify patients who are at increased risk of cardiac arrest based on their medical history, lifestyle factors, and other characteristics. This paper reviews the use of data mining algorithms for early prediction of cardiac arrest. The paper discusses the different data mining algorithms that have been used for this purpose, as well as the results of studies that have evaluated the effectiveness of these algorithms. The paper also discusses the challenges of using data mining for early prediction of cardiac arrest, and the future directions of research in this area. The paper concludes that data mining algorithms have the potential to improve early prediction of cardiac arrest. However, more research is needed to develop more accurate and reliable data mining algorithms. Additionally, more research is needed to develop methods for integrating data mining algorithms into clinical practice.
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Dissertations / Theses on the topic "History of data mining"

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Egas, Carlos A. "Methodology for Data Mining Customer Order History for Storage Assignment." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1345223808.

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Rosswog, James. "Improving classification of spatiotemporal data using adaptive history filtering." Diss., Online access via UMI:, 2007.

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Virkkala, Linda, and Johanna Haglund. "Modelling of patterns between operational data, diagnostic trouble codes and workshop history using big data and machine learning." Thesis, Uppsala universitet, Datalogi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279823.

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The work presented in this thesis is part of a large research and development project on condition-based maintenance for heavy trucks and buses at Scania. The aim of this thesis was to be able to predict the status of a component (the starter motor) using data mining methods and to create models that can predict the failure of that component. Based on workshop history data, error codes and operational data, three sets of classification models were built and evaluated. The first model aims to find patterns in a set of error codes, to see which codes are related to a starter motor failure. The second model aims to see if there are patterns in operational data that lead to the occurrence of an error code. Finally, the two data sets were merged and a classifier was trained and evaluated on this larger data set. Two machine learning algorithms were used and compared throughout the model building: AdaBoost and random forest. There is no statistically significant difference in their performance, and both algorithms had an error rate around ~13%, ~5% and ~13% for the three classification models respectively. However, random forest is much faster, and is therefore the preferable option for an industrial implementation. Variable analysis was conducted for the error codes and operational data, resulting in rankings of informative variables. From the evaluation metric precision, it can be derived that if our random forest model predicts a starter motor failure, there is a 85.7% chance that it actually has failed. This model finds 32% (the models recall) of the failed starter motors. It is also shown that four error codes; 2481, 2639, 2657 and 2597 have the highest predictive power for starter motor failure classification. For the operational data, variables that concern the starter motor lifetime and battery health are generally ranked as important by the models. The random forest model finds 81.9% of the cases where the 2481 error code occurs. If the random forest model predicts that the error code 2481 will occur, there is a 88.2% chance that it will. The classification performance was not increased when the two data sets were merged, indicating that the patterns detected by the two first classification models do not add value toone another.
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Jiang, Tianyu. "“Frankenstein Complex” in the Realm of Digital Humanities : Data Mining Classic Horror Cinema via Media History Digital Library (MHDL)." Thesis, Stockholms universitet, Filmvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-169638.

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This thesis addresses the complexity of digitalization and humanities research practices, with a specific focus on digital archives and film history research. I propose the term “Frankenstein Complex” to highlight and contextualize the epistemological collision and empirical challenges humanities scholars encounter when utilizing digital resources with digital methods. A particular aim of this thesis is to scrutinize digital archiving practices when using the Media History Digital Library (MHDL) as a case for a themed meta-inquiry on the preservation of and access to classic horror cinema in this particular digital venue. The project found conventional research methods, such as the close reading of classical cinema history, to be limiting. Instead, the project tried out a distant reading technique throughout the meta-inquiry to better interrogate with the massive volume of data generated by MHDL. Besides a general reassessment of debates in the digital humanities and themes relating to horror film culture, this thesis strives for a reflection on classic horror spectatorship through the lens of sexual identity, inspired by Sara Ahmed’s perspective on queer phenomenology. This original reading of horror history is facilitated by an empirical study of the digital corpus at hand, which in turn gives insights into the entangled relation between subjective identities and the appointed research contexts.
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Magnusson, John. "Finding time-based listening habits in users music listening history to lower entropy in data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300043.

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In a world where information, entertainment and e-commerce are growing rapidly in terms of volume and options, it can be challenging for individuals to find what they want. Search engines and recommendation systems have emerged as solutions, guiding the users. A typical example of this is Spotify, a music streaming company that utilises users listening data and other derived metrics to provide personalised music recommendation. Spotify has a hypothesis that external factors affect users listening preferences and that some of these external factors routinely affect the users, such as workout routines and commuting to work. This work aims to find time- based listening habits in users’ music listening history to decrease the entropy in the data, resulting in a better understanding of the users. While this work primarily targets listening habits, the method can, in theory, be applied on any time series-based dataset. Listening histories were split into hour vectors, vectors where each element represents the distribution of a label/genre played during an hour. The hour vectors allowed for a good representation of the data independent of the volume. In addition, it allowed for clustering, making it possible to find hours where similar music was played. Hour slots that routinely appeared in the same cluster became a profile, highlighting a habit. In the final implementation, a user is represented by a profile vector allowing different profiles each hour of a week. Several users were profiled with the proposed approach and evaluated in terms of decrease in Shannon entropy when profiled compared to when not profiled. On average, user entropy dropped by 9% with highs in the 50% and a small portion of users not experiencing any decrease. In addition, the profiling was evaluated by measuring cosine similarity across users listening history, resulting in a correlation between gain in cosine similarity and decrease in entropy. In conclusion, users become more predictable and interpretable when profiled. This knowledge can be used to understand users better or as a feature for recommender systems and other analysis.<br>I en värld där information, underhållning och e-handel har vuxit kraftig i form av volym och alternativ, har individer fått det svårare att hitta det som de vill ha. Sökmotorer och rekommendationssystem har vuxit fram som lösningar till detta problem och hjälpt individer att hitta rätt. Ett typexempel på detta är Spotify, en musikströmningstjänst som använder sig av användares lyssningsdata för att rekommendera musik och annan personalisering. Spotify har en hypotes att externa faktorer påverkar användares lyssningspreferenser, samt att vissa av dessa faktorer påverkar användaren rutinmässigt som till exempel träningsrutiner och pendlade till jobbet. Målet med detta arbete är att hitta tidsbaserade lyssningsvanor i användares musiklyssningshistorik för att sänka Shannon entropin i data, resulterande i en bättre förståelse av användarna. Arbetet är primärt gjort för att hitta lyssningsvanor, men metoden kan i teorin appliceras på valfri godtycklig tidsserie dataset. Lyssningshistoriken delades in i timvektorer, radvektorer med längden x där varje element representerar fördelningen av en etikett/ genre som spelas under en timme. Timvektorerna skapade möjligheten till att använda klusteranalys som användes för att hitta timmar där liknande musik spelats. Timvektorer som rutinmässigt hamnade i samma kluster blev profiler, som användes för att markera vanor. I den slutgiltiga produkten representeras en användare av en profilvektor som tillåter en användare att ha en profil för varje timme i veckan. Ett flertal användare blev profilerade med den föreslagna metoden och utvärderade i form av sänkning i entropi när de blev profilerade gentemot när de inte blev profilerade. I genomsnitt sänktes användarnas entropi med 9%, med några över användare 50%, samt ett fåtal som inte fick någon sänknings alls. Profilering blev även utvärderad genom att mäta cosinuslikhet över en användares lyssningshistorik. Detta resulterade i en korrelation mellan ökning i cosinuslikhet och sänkning i entropi vid användandet av profilering. Slutsatsen som kan dras är att användare blir mera förutsägbara och tolkbara när de har blivit profilerade. Denna kunskap kan användas till att förstå användare bättre eller användas som en del av ett rekommendationssystem eller annan analys.
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Hagward, Anders. "Using Git Commit History for Change Prediction : An empirical study on the predictive potential of file-level logical coupling". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172998.

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In recent years, a new generation of distributed version control systems have taken the place of the aging centralized ones, with Git arguably being the most popular distributed system today. We investigate the potential of using Git commit history to predict files that are often changed together. Specifically, we look at the rename tracking heuristic found in Git, and the impact it has on prediction performance. By applying a data mining algorithm to five popular GitHub repositories we extract logical coupling – inter-file dependencies not necessarily detectable by static analysis – on which we base our change prediction. In addition, we examine if certain commits are better suited for change prediction than others; we define a bug fix commit as a commit that resolves one or more issues in the associated issue tracking system and compare their prediction performance. While our findings do not reveal any notable differences in prediction performance when disregarding rename information, they suggest that extracting coupling from, and predicting on, bug fix commits in particular could lead to predictions that are both more accurate and numerous.<br>De senaste åren har en ny generation av distribuerade versionshanteringssystem tagit plats där tidigare centraliserade sådana huserat. I spetsen för dessa nya system går ett system vid namn Git. Vi undersöker potentialen i att nyttja versionshistorik från Git i syftet att förutspå filer som ofta redigeras ihop. I synnerhet synar vi Gits heuristik för att detektera när en fil flyttats eller bytt namn, någonting som torde vara användbart för att bibehålla historiken för en sådan fil, och mäter dess inverkan på prediktionsprestandan. Genom att applicera en datautvinningsalgoritm på fem populära GitHubprojekt extraherar vi logisk koppling – beroenden mellan filer som inte nödvändigtvis är detekterbara medelst statisk analys – på vilken vi baserar vår prediktion. Därtill utreder vi huruvida vissa Gitcommits är bättre lämpade för prediktion än andra; vi definierar en buggfixcommit som en commit som löser en eller flera buggar i den tillhörande buggdatabasen, och jämför deras prediktionsprestanda. Medan våra resultat ej kan påvisa några större prestandamässiga skillnader när flytt- och namnbytesinformationen ignorerades, indikerar de att extrahera koppling från, och prediktera på, enbart bugfixcommits kan leda till förutsägelser som är både mer precisa och mångtaliga.
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Matys, Filip. "Předpověď nových chyb pomocí dolování dat v historii výsledků testů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255448.

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Software projects go through a phase of maintenance and, in case of open source projects, through hard development process. Both of these phases are prone to regressions, meaning previously working parts of system do not work anymore. To avoid this behavior, systems are being tested with long test suites, which can be sometimes time consuming. For this reason, prediction models are developed to predict software regressions using historical testing data and code changes, to detect changes that can most likely cause regression and focus testing on such parts of code. But, these predictors rely on static code analysis without deeper semantic understanding of the code. Purpose of this master thesis is to create predictor, that relies not only on static code analysis, but provides decisions based on code semantics as well.
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Cressey, Michael. "The identification of early lead mining : environmental, archaeological and historical perspectives from Islay, Inner Hebrides, Scotland." Thesis, University of Edinburgh, 1996. http://hdl.handle.net/1842/33319.

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This thesis investigates whether lead mining can be detected using palaeoenvironmental data recovered from freshwater loch and marsh sediment. Using radiometric time-frames and geochernical analyses the environmental impact of 18th and 19th century mining on Islay, Inner Hebrides, Scotland, has been investigated. The model of known mining events thus produced has been used to assess previously unrecorded (early) lead mining activity. Previous mining in the area is suggested by 18th century accounts that record the presence of 1,000 "early" workings scattered over the north-east limestone region. While there is little to support the often repeated assertion that lead mining dates back to the Norse Period (circa lOll th centuries) it is clear that it may well have been an established industry prior to the time of the first historical records in the 16th century. In order to use a palaeoenvironmental approach to the question of mining history and its impact, the strategy has been to use integrated loch and catclunent units of study. The areas considered are; Loch Finlaggan, Loch Lossit, Loch Bharradail and a control site at Loch Leathann. Soil and sediment geochemical mapping has been used to assess the distribution of lead, zinc and copper within the catchments. Environmental pathways have been identified and influx of lead, zinc and copper to the loch sediment has been detennined through the analyses of cores from each loch basin. Archaeological fieldsurvey and the re-examination of the results from mineral prospecting data across the study region provides new evidence on the geographical extent and contaminatory effects of leadmining in this area. This study shows how the effect of lead mining can be identified in the palaeoenvironrnental record from circa 1367 AD onwards, so mining in Islay does indeed predate the earliest known archaeological and historical records.
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Mrázek, Michal. "Data mining." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400441.

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The aim of this master’s thesis is analysis of the multidimensional data. Three dimensionality reduction algorithms are introduced. It is shown how to manipulate with text documents using basic methods of natural language processing. The goal of the practical part of the thesis is to process real-world data from the internet forum. Posted messages are transformed to the numerical representation, then to two-dimensional space and visualized. Later on, topics of the messages are discovered. In the last part, a few selected algorithms are compared.
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Payyappillil, Hemambika. "Data mining framework." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3807.

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Thesis (M.S.)--West Virginia University, 2005<br>Title from document title page. Document formatted into pages; contains vi, 65 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 64-65).
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Books on the topic "History of data mining"

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M, Spehn E., and Körner Christian 1949-, eds. Data mining for global trends in mountain biodiversity. CRC Press, 2010.

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Nazarov, Dmitriy, and Anton Kopnin. Information technologies in professional activity: data mining and business analytics. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2110964.

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The textbook offers a comprehensive look covering a wide range of topics related to the role of information technology in the modern world. Starting with an overview of the history of information technology and its evolution, the book introduces the reader to professions in the field of information and digital technologies, emphasizing them in various professional fields. The content of the textbook includes a detailed analysis of key concepts such as Data Science, Data Mining and Machine Learning, and their role in healthcare, law, education, science and business. The textbook provides a detailed overview of Data Science methods and algorithms, including teaching methods with and without a teacher, as well as specific methods such as Dematel. In the section on business intelligence tools, special attention is paid to Yandex Cloud DataLens, its data analysis functionality, and practical recommendations on registration, the use of intelligent detection of patterns in data, visualization and the development of analytical panels are provided. The section of the textbook devoted to the R and Python programming languages contains recommendations on the use of the R and Python programming languages for statistical data analysis, a description of the main data types, operations, and specific algorithms used for analytical purposes. This section provides a quick guide on how to use the RStudio and PyCharm tools. The final section is devoted to the application of data analysis tools in real projects, providing the reader with the opportunity to immerse themselves in the world of data processing using advanced technologies and techniques. The presentation of the material of each chapter is accompanied by control questions and tests to consolidate the theoretical material, located at the end of the chapter. This textbook will be an indispensable resource for students, professionals and researchers who want to deeply understand and apply information and digital technologies in their professional activities.
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Hibbard, W. R. Virginia coal: An abridged history and complete data manual of Virginia coal production/consumption from 1748 to 1988. Virginia Center for Coal & Energy Research, Virginia Polytechnic Institute & State University, 1990.

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Smith, D. Historic data inventory of the Shasta County Interlakes Special Recreation Management Area. Edited by Ritter Eric W, United States. Bureau of Land Management. Redding Area Office, United States. Bureau of Land Management. California State Office, and United States. Forest Service. Pacific Southwest Region. Bureau of Land Management, 1995.

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service), SpringerLink (Online, ed. Modern Issues and Methods in Biostatistics. Springer Science+Business Media, LLC, 2011.

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Xu, Yue, Rosalind Wang, Anton Lord, et al., eds. Data Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8531-6.

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Dulli, Susi, Sara Furini, and Edmondo Peron. Data mining. Springer Milan, 2009. http://dx.doi.org/10.1007/978-88-470-1163-2.

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Stahlbock, Robert, Sven F. Crone, and Stefan Lessmann, eds. Data Mining. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1280-0.

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Islam, Rafiqul, Yun Sing Koh, Yanchang Zhao, et al., eds. Data Mining. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6661-1.

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Boo, Yee Ling, David Stirling, Lianhua Chi, Lin Liu, Kok-Leong Ong, and Graham Williams, eds. Data Mining. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0292-3.

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Book chapters on the topic "History of data mining"

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Chen, Xia, Minqiang Li, Wei Zhao, and Ding-Yi Chen. "Discovering Conceptual Page Hierarchy of a Web Site from User Traversal History." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_64.

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Dzuba, Alexandr, and Dmitry Bugaychenko. "Mining Users Playbacks History for Music Recommendations." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08979-9_31.

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Pearce, Adrian J., and Paul Heggarty. "“Mining the Data” on the Huancayo-Huancavelica Quechua Frontier." In History and Language in the Andes. Palgrave Macmillan US, 2011. http://dx.doi.org/10.1057/9780230370579_5.

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Liu, Ruifang, Changcun Li, and Qiwen Jin. "Research on Data Mining in Remote Learning System of Party History Information." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33030-8_64.

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Linton, Debra L., Elizabeth Ellwood, Lisa D. White, Natalie F. Douglas, and Anna K. Monfils. "Experiments in Data Mining: Using Digitized Natural History Collections to Introduce Biology Students to Data Science." In Trends in Teaching Experimentation in the Life Sciences. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98592-9_7.

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Xiong, Yan, Ze Luo, Baoping Yan, Diann J. Prosser, and John Y. Takekawa. "GPS Location History Data Mining and Anomalous Detection: The Scenario of Bar-Headed Geese Migration." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34522-7_44.

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Nishimura, Kunihiro, and Michitaka Hirose. "The Study of Past Working History Visualization for Supporting Trial and Error Approach in Data Mining." In Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73345-4_37.

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Ogasawara, Mitsuhiro, Hiroki Sugimori, Yukiyasu Iida, and Katsumi Yoshida. "Analysis Between Lifestyle, Family Medical History and Medical Abnormalities Using Data Mining Method – Association Rule Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552451_22.

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Xiong, Yan, Ze Luo, Baoping Yan, Diann J. Prosser, and John Y. Takekawa. "Erratum to: GPS Location History Data Mining and Anomalous Detection: The Scenario of Bar-Headed Geese Migration." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34522-7_106.

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Goldman, Jeffrey A., Wesley Chu, D. Stott Parker, and Robert M. Goldman. "TDDA, a Data Mining Tool for Text Databases: A Case History in a Lung Cancer Text Database." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49292-5_56.

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Conference papers on the topic "History of data mining"

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Raley, Randy E., and Morgan C. Wang. "Data Warehouse & Data Mining Techniques for Airframe Corrosion Control." In CORROSION 1999. NACE International, 1999. https://doi.org/10.5006/c1999-99237.

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Abstract The Solid Rocket Boosters of the Space Shuttle have very unique and critical requirements as airframe structures. They support the entire shuttle stack, absorb stresses of main engine start, experience water impact at greater than 60 miles per hour, and remain immersed in salt water for approximately 48 hours. In addition, the maintenance and deployment of the hardware takes place in a seacoast environment. In order to maintain the integrity of the hardware, inspections are performed on the entire structure after each flight to locate corrosion for remediation and analysis. The data for these inspections has been kept graphically on paper and descriptively on standard spreadsheet software. This paper describes the development of a data warehouse that will be used to facilitate inspections through a graphical interface, store all the data in flexible format and allow the mining of the data for new information on the structure. Specifically, the software will be used to identify areas where the corrosion protection design is deficient, monitor the effect of changes to the corrosion protection system, and trend the damage accumulation for life prediction. This is the first phase of implementation of a data warehouse on the structures. Work is also in progress to allow the inclusion of maintenance history, problem reports, materials process and traceability data, and to support the structural analysis of the hardware. The ultimate driver of this effort is increased system reliability by the transformation of currently available data, using data mining techniques, into information, which can be used to make better resource management decisions.
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He, Jing. "Advances in Data Mining: History and Future." In 2009 Third International Symposium on Intelligent Information Technology Application. IEEE, 2009. http://dx.doi.org/10.1109/iita.2009.204.

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Sefer, Emre, and Carl Kingsford. "Diffusion Archaeology for Diffusion Progression History Reconstruction." In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.135.

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Oskin, A., and D. Oskin. "About the curriculum of the course “Data Mining – Data Mining” for undergraduates of the specialty “History”." In Historical research in the context of data science: Information resources, analytical methods and digital technologies. LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1847.978-5-317-06529-4/447-453.

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The article discusses the curriculum of the course “Data Mining – Data Mining” for graduate students studying in the specialty “History”. The definition of the term “Data Mining” is given, the areas of application are listed and the importance of mastering these technologies by undergraduates of this specialty is emphasized. The content of the lecture component of the discipline, laboratory workshop is considered, a list of useful Internet resources is provided
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Takada, Kurihara, Hirotsu, and Sugawara. "Proximity mining: finding proximity using sensor data history." In Proceedings DARPA Information Survivability Conference and Exposition MCSA-03. IEEE, 2003. http://dx.doi.org/10.1109/mcsa.2003.1240774.

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Tsumoto, Shusaku, Shoji Hirano, Hidenao Abe, and Yuko Tsumoto. "Temporal data mining in history data of hospital information systems." In 2011 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2011. http://dx.doi.org/10.1109/icsmc.2011.6084029.

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Nakamura, Shoichi, Kaname Nozaki, Hiroki Nakayama, Yasuhiko Morimoto, and Youzou Miyadera. "Sequential Pattern Mining System for Analysis of Programming Learning History." In 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS). IEEE, 2015. http://dx.doi.org/10.1109/dsdis.2015.120.

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Chen, Xiaopeng, Dianxi Shi, Banghui Zhao, and Fan Liu. "Mining Individual Mobility Patterns Based on Location History." In 2016 IEEE First International Conference on Data Science in Cyberspace (DSC). IEEE, 2016. http://dx.doi.org/10.1109/dsc.2016.52.

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Ye, Yang, Yu Zheng, Yukun Chen, Jianhua Feng, and Xing Xie. "Mining Individual Life Pattern Based on Location History." In 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware. IEEE, 2009. http://dx.doi.org/10.1109/mdm.2009.11.

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Jin, Ning, and Wei Wang. "LTS: Discriminative subgraph mining by learning from search history." In 2011 IEEE International Conference on Data Engineering (ICDE 2011). IEEE, 2011. http://dx.doi.org/10.1109/icde.2011.5767922.

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Reports on the topic "History of data mining"

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Hoyt, Robert, Hui-Min Chung, Brent Hutfless, Justice Mbizo, and Courtney Rice. Creating a Web-Based Family History Questionnaire for Data Mining. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada578129.

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Block, Sharon. What, Where, When and Sometimes Why: Data Mining Two Decades of Women’s History Abstracts (annotated version). Roy Rosenzweig Center for History and New Media, 2021. http://dx.doi.org/10.31835/ma.2021.06.

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Merritt, R. D. History of Alaskan coal mining. Alaska Division of Geological & Geophysical Surveys, 1988. http://dx.doi.org/10.14509/1349.

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Merritt, R. D. Chronicle of Alaska coal-mining history. Alaska Division of Geological & Geophysical Surveys, 1986. http://dx.doi.org/10.14509/1256.

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Lee, K., H. Kargupta, B. G. Stafford, K. L. Buescher, and B. Ravindran. Data mining. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/334314.

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Kramer, Mitchell. Customer Data Mining. Patricia Seybold Group, 2004. http://dx.doi.org/10.1571/psgp5-27-04cc.

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Kramer, Mitchell. Data Mining at Work. Patricia Seybold Group, 2004. http://dx.doi.org/10.1571/psgp6-10-04cc.

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Brown, David A., John Hirdt, and Michal Herman. Data mining the EXFOR database. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1122776.

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Lu, Xiaomeng, Robert Stambaugh, and Yu Yuan. Anomalies Abroad: Beyond Data Mining. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23809.

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Davidson, George S., Jana Strasburg, David Stampf, et al. Data mining for ontology development. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/992328.

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