Relevant bibliographies by topics / Unstructured data mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Unstructured data mining'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Unstructured data mining"

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Rajalakshmi, Thiruthuraipondi Natarajan. "Data Mining from Unstructured Documents." International Journal on Science and Technology 14, no. 3 (2023): 1–6. https://doi.org/10.5281/zenodo.14631493.

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Data Mining is the process of identifying and extracting valuable data by scanning through large volumes of structured and unstructured data, which would form the base for further processing using data analytics tools for cleansing, categorization and organization, etc. This source data might not fit to a certain template and can be of any format ranging from plan test to media files and it is the responsibility of the mining process to understand the message, extract relevant information and finally convert to a standard format. Prior to its final stage, these data undergo several rounds to cleansing to eliminate irrelevant information and pick the right set of data intended by the organization with the best turnaround time possible. At each stage of the analysis, the data needs to gets cleaner and distinctive and provide a vision as to the areas it will be used.This document provides insight on data mining and its potential impact in market. This explores the various sources and the type of data that might be associated with it and how to cleanse and various ways the information can be used for the development of a retail business. This also provides guidance on the patten recognition and the proper compartmentalization of the data so that it is readily available to the target groups for research and marketing
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Muhammad, Aoun. "Comparative Analysis of Text Mining Techniques for News Article Summarization." LC International Journal of STEM (ISSN: 2708-7123) 4, no. 1 (2023): 52–63. https://doi.org/10.5281/zenodo.7893329.

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Text mining research paper is a scientific study that focuses on the development and application of text mining techniques for extracting valuable information from unstructured textual data. The paper discusses the challenges of working with unstructured data and the need for advanced text mining techniques to address these challenges. The paper outlines the various steps involved in the text mining process, such as data preprocessing, text representation, and feature selection. It discusses the importance of selecting appropriate algorithms for different types of text mining tasks, including text classification, clustering, sentiment analysis, and topic modeling. The paper also discusses the challenges of evaluating text mining models, including issues related to data quality, model performance, and interpretability. It highlights the importance of using appropriate evaluation metrics and techniques to ensure the reliability and validity of the results. Finally, the paper provides case studies and real-world examples of text mining applications in various domains such as healthcare, social media analysis, and financial analysis. It emphasizes the potential of text mining to provide valuable insights and knowledge that can be used to support decision-making in different industries. Overall, the paper highlights the importance of text mining as a powerful tool for analyzing unstructured textual data and provides a comprehensive overview of the key techniques and challenges in this field.
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Anisha, S., and S. Thiyagarajan Dr. "Analytical Study on Unstructured Data Management in Application Data Base through NLP and Datamining." Analytical Study on Unstructured Data Management in Application Data Base through NLP and Datamining 9, no. 1 (2024): 5. https://doi.org/10.5281/zenodo.10634318.

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Business Organizations are flooded with large pool of unstructured data. Loading these data into business database warranted a lot of processes. Companies having BPO and KPO are working for converting unstructured data into their software database with huge resources through programming, with multiple queries and users. To deal with such complex and perplexed situations need an automated system in place and thereby saving a large amount of time and resources. The aim of the present research was to analyse methodically, the technical works relating to the application of data mining, artificial intelligence (AI) and machine learning (ML) in the software industry. In this paper combining with different disciplines of data mining techniques, ML and NLP. Objective of this paper is to improve the organization's business intelligence process through maximum exploitation of unstructured data owned by them. This paper primarily attempts to examine the applicability of combination of data mining techniques, NLP and ML in handling unstructured data and reduces the burden on users by minimizing the usage of multiple queries and make them user-friendly to extract data from large database. Keywords:- Application Database, Data mining, ML, NLP.
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Lomotey, Richard K., and Ralph Deters. "Unstructured data mining: use case for CouchDB." International Journal of Big Data Intelligence 2, no. 3 (2015): 168. http://dx.doi.org/10.1504/ijbdi.2015.070597.

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Reshmy, A. K., and D. Paulraj. "Data mining of unstructured big data in cloud computing." International Journal of Business Intelligence and Data Mining 12, no. 3/4 (2017): 1. http://dx.doi.org/10.1504/ijbidm.2017.10004683.

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Reshmy, A. K., and D. Paulraj. "Data mining of unstructured big data in cloud computing." International Journal of Business Intelligence and Data Mining 13, no. 1/2/3 (2018): 147. http://dx.doi.org/10.1504/ijbidm.2018.088430.

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Oh, Tae-Jin, and Anthony. "New and Fast Emerging Advance Structure of Text Mining from Unstructured Data." Bonfring International Journal of Industrial Engineering and Management Science 7, no. 2 (2017): 13–16. http://dx.doi.org/10.9756/bijiems.8325.

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Singh, Shashi Pal, Ajai Kumar, Rachna Awasthi, Neetu Yadav, and Shikha Jain. "Intelligent Bilingual Data Extraction and Rebuilding Using Data Mining for Big Data." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 513–18. http://dx.doi.org/10.1166/jctn.2020.8699.

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In today’s World there exists various source of data in various formats (file formats), different structure, different types and etc. which is a hug collection of unstructured over the internet or social media. This gives rise to categorization of data as unstructured, semi structured and structured data. Data that exist in irregular manner without any particular schema are referred as unstructured data which is very difficult to process as it consists of irregularities and ambiguities. So, we are focused on Intelligent Processing Unit which converts unstructured big data into intelligent meaningful information. Intelligent text extraction is a technique that automatically identifies and extracts text from file format. The system consists of different stages which include the pre-processing, keyphase extraction techniques and transformation for the text extraction and retrieve structured data from unstructured data. The system consists multiple method/approach give better result. We are currently working in various file formats and converting the file format into DOCX which will come in the form of the un-structure Form, and then we will obtain that file in the structure form with the help of intelligent Pre-processing. The pre-process stages that triggers the unstructured data/corpus into structured data converting into meaning full. The Initial stage is the system remove the stop word, unwanted symbols noisy data and line spacing. The second stage is Data Extraction from various sources of file or types of files into proper format plain text. The then in third stage we transform the data or information from one format to another for the user to understand the data. The final step is rebuilding the file in its original format maintaining tag of the files. The large size files are divided into sub small size file to executed the parallel processing algorithms for fast processing of larger files and data. Parallel processing is a very important concept for text extraction and with its help; the big file breaks in a small file and improves the result. Extraction of data is done in Bilingual language, and represent the most relevant information contained in the document. Key-phase extraction is an important problem of data mining, Knowledge retrieval and natural speech processing. Keyword Extraction technique has been used to abstract keywords that exclusively recognize a document. Rebuilding is an important part of this project and we will use the entire concept in that file format and in the last, we need the same format which we have done in that file. This concept is being widely used but not much work of the work has been done in the area of developing many functionalities under one tool, so this makes us feel the requirement of such a tool which can easily and efficiently convert unstructured files into structured one.
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Suneeta, Salimath. "Need of Data Mining in Search Engine Optimization." Journal of Management Commerce Engineering and IT (JMCEI) 1, no. 2 (2022): 9–12. https://doi.org/10.5281/zenodo.7265250.

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Analyzing massive data sets to discover novel traffic patterns and uncover market possibilities may be summed up as data mining SEO activity. These specialty trends are then used to target a certain user group more effectively with a service or product. Companies employ data mining as a method to transform unstructured data into information that is valuable. Businesses may learn more about their consumers to create more successful marketing campaigns, boost sales, and cut expenses by employing software to seek for patterns in massive volumes of data. Organizations use data mining to find patterns in data that might provide insights into their operational needs. Both business intelligence and data science require it. Organizations may utilise a variety of data mining approaches to transform unstructured data into insights that can be put to use.
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Ali, Hameed Yassir, A. Mohammed Ali, Abdul-Jabbar Alkhazraji Adel, Emad Hameed Mustafa, Saad Talib Mohammed, and Faeq Ali Mohanad. "Sentimental classification analysis of polarity multi-view textual data using data mining techniques." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5526–34. https://doi.org/10.11591/ijece.v10i5.pp5526-5534.

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The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
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Dissertations / Theses on the topic "Unstructured data mining"

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Bala, Saimir. "Mining Projects from Structured and Unstructured Data." Jens Gulden, Selmin Nurcan, Iris Reinhartz-Berger, Widet Guédria, Palash Bera, Sérgio Guerreiro, Michael Fellman, Matthias Weidlich, 2017. http://epub.wu.ac.at/7205/1/ProjecMining%2DCamera%2DReady.pdf.

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Companies working on safety-critical projects must adhere to strict rules imposed by the domain, especially when human safety is involved. These projects need to be compliant to standard norms and regulations. Thus, all the process steps must be clearly documented in order to be verifiable for compliance in a later stage by an auditor. Nevertheless, documentation often comes in the form of manually written textual documents in different formats. Moreover, the project members use diverse proprietary tools. This makes it difficult for auditors to understand how the actual project was conducted. My research addresses the project mining problem by exploiting logs from project-generated artifacts, which come from software repositories used by the project team.
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Bojduj, Brett N. "Extraction of Causal-Association Networks from Unstructured Text Data." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/138.

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Causality is an expression of the interactions between variables in a system. Humans often explicitly express causal relations through natural language, so extracting these relations can provide insight into how a system functions. This thesis presents a system that uses a grammar parser to extract causes and effects from unstructured text through a simple, pre-defined grammar pattern. By filtering out non-causal sentences before the extraction process begins, the presented methodology is able to achieve a precision of 85.91% and a recall of 73.99%. The polarity of the extracted relations is then classified using a Fisher classifier. The result is a set of directed relations of causes and effects, with polarity as either increasing or decreasing. These relations can then be used to create networks of causes and effects. This “Causal-Association Network” (CAN) can be used to aid decision-making in complex domains such as economics or medicine, that rely upon dynamic interactions between many variables.
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King, Michael Allen. "Ensemble Learning Techniques for Structured and Unstructured Data." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/51667.

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This research provides an integrated approach of applying innovative ensemble learning techniques that has the potential to increase the overall accuracy of classification models. Actual structured and unstructured data sets from industry are utilized during the research process, analysis and subsequent model evaluations. The first research section addresses the consumer demand forecasting and daily capacity management requirements of a nationally recognized alpine ski resort in the state of Utah, in the United States of America. A basic econometric model is developed and three classic predictive models evaluated the effectiveness. These predictive models were subsequently used as input for four ensemble modeling techniques. Ensemble learning techniques are shown to be effective. The second research section discusses the opportunities and challenges faced by a leading firm providing sponsored search marketing services. The goal for sponsored search marketing campaigns is to create advertising campaigns that better attract and motivate a target market to purchase. This research develops a method for classifying profitable campaigns and maximizing overall campaign portfolio profits. Four traditional classifiers are utilized, along with four ensemble learning techniques, to build classifier models to identify profitable pay-per-click campaigns. A MetaCost ensemble configuration, having the ability to integrate unequal classification cost, produced the highest campaign portfolio profit. The third research section addresses the management challenges of online consumer reviews encountered by service industries and addresses how these textual reviews can be used for service improvements. A service improvement framework is introduced that integrates traditional text mining techniques and second order feature derivation with ensemble learning techniques. The concept of GLOW and SMOKE words is introduced and is shown to be an objective text analytic source of service defects or service accolades.<br>Ph. D.
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Al-Azzam, Omar Ghazi. "Mining for Significant Information from Unstructured and Structured Biological Data and Its Applications." Diss., North Dakota State University, 2012. https://hdl.handle.net/10365/26509.

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Massive amounts of biological data are being accumulated in science. Searching for significant meaningful information and patterns from different types of data is necessary towards gaining knowledge from these large amounts of data available to users. However, data mining techniques do not normally deal with significance. Integrating data mining techniques with standard statistical procedures provides a way for mining statistically signi- ficant, interesting information from both structured and unstructured data. In this dissertation, different algorithms for mining significant biological information from both unstructured and structured data are proposed. A weighted-density-based approach is presented for mining item data from unstructured textual representations. Different algorithms in the area of radiation hybrid mapping are developed for mining significant information from structured binary data. The proposed algorithms have different applications in the ordering problem in radiation hybrid mapping including: identifying unreliable markers, and building solid framework maps. Effectiveness of the proposed algorithms towards improving map stability is demonstrated. Map stability is determined based on resampling analysis. The proposed algorithms deal effectively and efficiently with multidimensional data and also reduce computational cost dramatically. Evaluation shows that the proposed algorithms outperform comparative methods in terms of both accuracy and computation cost.
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Yaakub, Mohd Ridzwan. "Integration of Opinion Mining into customer analysis model." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/85084/1/Mohd%20Ridzwan_Yaakub_Thesis.pdf.

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This research proposes a multi-dimensional model for Opinion Mining, which integrates customers' characteristics and their opinions about products (or services). Customer opinions are valuable for companies to deliver right products or services to their customers. This research presents a comprehensive framework to evaluate opinions' orientation based on products' hierarchy attributes. It also provides an alternative way to obtain opinion summaries for different groups of customers and different categories of produces.
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葉立志 and Chi-lap Yip. "Discovering patterns in databases: the cases for language, music, and unstructured data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31242649.

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Yip, Chi-lap. "Discovering patterns in databases the cases for language, music, and unstructured data /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2240112X.

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Popescu, Ana-Maria. "Information extraction from unstructured web text /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6935.

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Vikholm, Oskar. "Dealing with unstructured data : A study about information quality and measurement." Thesis, Uppsala universitet, Institutionen för informatik och media, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-255214.

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Many organizations have realized that the growing amount of unstructured text may contain information that can be used for different purposes, such as making decisions. Organizations can by using so-called text mining tools, extract information from text documents. For example within military and intelligence activities it is important to go through reports and look for entities such as names of people, events, and the relationships in-between them when criminal or other interesting activities are being investigated and mapped. This study explores how information quality can be measured and what challenges it involves. It is done on the basis of Wang and Strong (1996) theory about how information quality can be measured. The theory is tested and discussed from empirical material that contains interviews from two case organizations. The study observed two important aspects to take into consideration when measuring information quality: context dependency and source criticism. Context dependency means that the context in which information quality should be measured in must be defined based on the consumer’s needs. Source criticism implies that it is important to take the original source into consideration, and how reliable it is. Further, data quality and information quality is often used interchangeably, which means that organizations needs to decide what they really want to measure. One of the major challenges in developing software for entity extraction is that the system needs to understand the structure of natural language, which is very complicated.<br>Många organisationer har insett att den växande mängden ostrukturerad text kan innehålla information som kan användas till flera ändamål såsom beslutsfattande. Genom att använda så kallade text-mining verktyg kan organisationer extrahera information från textdokument. Inom till exempel militär verksamhet och underrättelsetjänst är det viktigt att kunna gå igenom rapporter och leta efter exempelvis namn på personer, händelser och relationerna mellan dessa när brottslig eller annan intressant verksamhet undersöks och kartläggs. I studien undersöks hur informationskvalitet kan mätas och vilka utmaningar det medför. Det görs med utgångspunkt i Wang och Strongs (1996) teori om hur informationskvalité kan mätas. Teorin testas och diskuteras utifrån ett empiriskt material som består av intervjuer från två fall-organisationer. Studien uppmärksammar två viktiga aspekter att ta hänsyn till för att mäta informationskvalitét; kontextberoende och källkritik. Kontextberoendet innebär att det sammanhang inom vilket informationskvalitét mäts måste definieras utifrån konsumentens behov. Källkritik innebär att det är viktigt att ta hänsyn informationens ursprungliga källa och hur trovärdig den är. Vidare är det viktigt att organisationer bestämmer om det är data eller informationskvalitét som ska mätas eftersom dessa två begrepp ofta blandas ihop. En av de stora utmaningarna med att utveckla mjukvaror för entitetsextrahering är att systemen ska förstå uppbyggnaden av det naturliga språket, vilket är väldigt komplicerat.
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Sequeira, José Francisco Rodrigues. "Automatic knowledge base construction from unstructured text." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17910.

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Mestrado em Engenharia de Computadores e Telemática<br>Taking into account the overwhelming number of biomedical publications being produced, the effort required for a user to efficiently explore those publications in order to establish relationships between a wide range of concepts is staggering. This dissertation presents GRACE, a web-based platform that provides an advanced graphical exploration interface that allows users to traverse the biomedical domain in order to find explicit and latent associations between annotated biomedical concepts belonging to a variety of semantic types (e.g., Genes, Proteins, Disorders, Procedures and Anatomy). The knowledge base utilized is a collection of MEDLINE articles with English abstracts. These annotations are then stored in an efficient data storage that allows for complex queries and high-performance data delivery. Concept relationship are inferred through statistical analysis, applying association measures to annotated terms. These processes grant the graphical interface the ability to create, in real-time, a data visualization in the form of a graph for the exploration of these biomedical concept relationships.<br>Tendo em conta o crescimento do número de publicações biomédicas a serem produzidas todos os anos, o esforço exigido para que um utilizador consiga, de uma forma eficiente, explorar estas publicações para conseguir estabelecer associações entre um conjunto alargado de conceitos torna esta tarefa exaustiva. Nesta disertação apresentamos uma plataforma web chamada GRACE, que providencia uma interface gráfica de exploração que permite aos utilizadores navegar pelo domínio biomédico em busca de associações explícitas ou latentes entre conceitos biomédicos pertencentes a uma variedade de domínios semânticos (i.e., Genes, Proteínas, Doenças, Procedimentos e Anatomia). A base de conhecimento usada é uma coleção de artigos MEDLINE com resumos escritos na língua inglesa. Estas anotações são armazenadas numa base de dados que permite pesquisas complexas e obtenção de dados com alta performance. As relações entre conceitos são inferidas a partir de análise estatística, aplicando medidas de associações entre os conceitos anotados. Estes processos permitem à interface gráfica criar, em tempo real, uma visualização de dados, na forma de um grafo, para a exploração destas relações entre conceitos do domínio biomédico.
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Books on the topic "Unstructured data mining"

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Inmon, William H. Tapping into unstructured data: Integrating unstructured data and textual analytics into business intelligence. Prentice Hall, 2008.

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Feldman, Ronen. The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press, 2007.

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1965-, Sanger James, ed. The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press, 2006.

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Holzinger, Andreas. Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data: Third International Workshop, HCI-KDD 2013, Held at SouthCHI 2013, Maribor, Slovenia, July 1-3, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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Inmon, William, and Anthony Nesavich. Tapping into Unstructured Data: Integrating Unstructured Data and Textual Analytics into Business Intelligence. Pearson Education, 2007.

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Spangler, Scott. Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation. Taylor & Francis Group, 2015.

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Feldman, Ronen, and James Sanger. Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 2002.

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Feldman, Ronen, and James Sanger. Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 2006.

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Feldman, Ronen, and James Sanger. Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 2009.

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Feldman, Ronen, and James Sanger. Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 2007.

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Book chapters on the topic "Unstructured data mining"

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Yu, Chong Ho Alex. "Text Mining Structure the Unstructured." In Data Mining and Exploration. CRC Press, 2022. http://dx.doi.org/10.1201/9781003153658-11.

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Lourentzou, Ismini, Alfredo Alba, Anni Coden, Anna Lisa Gentile, Daniel Gruhl, and Steve Welch. "Mining Relations from Unstructured Content." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93037-4_29.

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Gong, Shucheng, and Hongyan Liu. "Constructing Decision Trees for Unstructured Data." In Advanced Data Mining and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14717-8_37.

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Kuri-Morales, Angel. "Mining Unstructured Data via Computational Intelligence." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27060-9_43.

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Balinsky, Alexander, Helen Balinsky, and Steven Simske. "Detecting Unusual Behaviour and Mining Unstructured Data." In UK Success Stories in Industrial Mathematics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25454-8_23.

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Jabbari, Simin, and Kilian Stoffel. "FCA-Based Ontology Learning from Unstructured Textual Data." In Mining Intelligence and Knowledge Exploration. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05918-7_1.

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Decker, Dyan, Alexandre Blanc, John Loveland, and Mona Clayton. "Data Mining: Analysis of Structured and Unstructured Information." In A Guide to Forensic Accounting Investigation. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119200048.ch17.

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Rajman, Martin, and Romaric Besançon. "Text Mining - Knowledge extraction from unstructured textual data." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72253-0_64.

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Löffler, Ralf. "Opinion Mining from Unstructured Web 2.0 Data: A Dicode Use Case." In Studies in Big Data. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02612-1_9.

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Garg, Harshi, and Niranjan Lal. "Data Analysis: Opinion Mining and Sentiment Analysis of Opinionated Unstructured Data." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1813-9_25.

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Conference papers on the topic "Unstructured data mining"

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Sun, Zounachuan, Ranjan Satapathy, Daixue Guo, et al. "Information Extraction: Unstructured to Structured for ESG Reports." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00068.

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Zhou, Zhenwei, Junbin Liu, Shilie He, and He Li. "Fault Information Mining in Avionics Components Based on Unstructured Data." In 2023 14th International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE, 2023. http://dx.doi.org/10.1109/icrms59672.2023.00052.

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Xie, Tong, Hanzhi Zhang, Shaozhou Wang, et al. "ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00126.

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Feldman, Ronen. "Mining unstructured data." In Tutorial notes of the fifth ACM SIGKDD international conference. ACM Press, 1999. http://dx.doi.org/10.1145/312179.312192.

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Bacchelli, Alberto, Nicolas Bettenburg, Latifa Guerrouj, and Sonia Haiduc. "3rd workshop on Mining Unstructured Data." In 2013 20th Working Conference on Reverse Engineering (WCRE). IEEE, 2013. http://dx.doi.org/10.1109/wcre.2013.6671333.

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Bettenburg, Nicolas, and Bram Adams. "Workshop on Mining Unstructured Data (MUD) because "Mining Unstructured Data is Like Fishing in Muddy Waters"!" In 2010 17th Working Conference on Reverse Engineering (WCRE). IEEE, 2010. http://dx.doi.org/10.1109/wcre.2010.39.

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Bacchelli, Alberto, Nicolas Bettenburg, and Latifa Guerrouj. "Workshop on Mining Unstructured Data (MUD) ... Because "Mining Unstructured Data is Like Fishing in Muddy Waters"!" In 2012 19th Working Conference on Reverse Engineering (WCRE). IEEE, 2012. http://dx.doi.org/10.1109/wcre.2012.67.

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Kraft, Volker. "Analyzing unstructured data: text analytics in JMP." In Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17204.

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As much as 80% of all data is unstructured but still has exploitable information available. For example, unstructured text data could result from comment fields in surveys or incident reports. You want to explore this unstructured text to better understand the information that it contains. Text Mining, based on a transformation of free text into numerical summaries, can pave the way for new findings. This example of the new text mining feature in JMP starts with a multi-step text preparation using techniques like stemming and tokenizing. This data curation is pivotal for the subsequent analysis phase, exploring data clusters and semantics. Finally, combining text mining results with other structured data takes familiar multivariate analysis and predictive modeling to a next level.
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Lomotey, Richard K., and Ralph Deters. "Topics and Terms Mining in Unstructured Data Stores." In 2013 IEEE 16th International Conference on Computational Science and Engineering (CSE). IEEE, 2013. http://dx.doi.org/10.1109/cse.2013.129.

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Lomotey, Richard K., and Ralph Deters. "Real-Time Effective Framework for Unstructured Data Mining." In 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2013. http://dx.doi.org/10.1109/trustcom.2013.131.

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Reports on the topic "Unstructured data mining"

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Ansari, S. M., E. M. Schetselaar, and J. A. Craven. Three-dimensional magnetotelluric modelling of the Lalor volcanogenic massive-sulfide deposit, Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328003.

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Unconstrained magnetotelluric inversion commonly produces insufficient inherent resolution to image ore-system fluid pathways that were structurally thinned during post-emplacement tectonic activity. To improve the resolution in these complex environments, we synthesized the 3-D magnetotelluric (MT) response for geologically realistic models using a finite-element-based forward-modelling tool with unstructured meshes and applied it to the Lalor volcanogenic massive-sulfide deposit in the Snow Lake mining camp, Manitoba. This new tool is based on mapping interpolated or simulated resistivity values from wireline logs onto unstructured tetrahedral meshes to reflect, with the help of 3-D models obtained from lithostratigraphic and lithofacies drillhole logs, the complexity of the host-rock geological structure. The resulting stochastic model provides a more realistic representation of the heterogeneous spatial distribution of the electric resistivity values around the massive, stringer, and disseminated sulfide ore zones. Both models were combined into one seamless tetrahedral mesh of the resistivity field. To capture the complex resistivity distribution in the geophysical forward model, a finite-element code was developed. Comparative analyses of the forward models with MT data acquired at the Earth's surface show a reasonable agreement that explains the regional variations associated with the host rock geological structure and detects the local anomalies associated with the MT response of the ore zones.
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