Text Mining and Information Extraction

Ben-Dov, Moty; Feldman, Ronen

doi:10.1007/0-387-25465-x_38

Cited by 11 publications

(4 citation statements)

References 56 publications

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“… 35 This has been successfully applied to analyze multisource data trends in fields ranging from banking 36 to healthcare 37 and could also simplify data collection for use in CBM submissions. Furthermore, text mining, which uses natural language processing to extract information from large amounts of unstructured text data, 38 can be used to complement human judgment in the compilation and analysis of CBM submissions, which would otherwise be tedious. These applications are illustrated using large language models to successfully conduct structured information extraction on complex scientific text, although it should be accompanied by human oversight.…”

Section: Policy Proposalsmentioning

confidence: 99%

A Modular-Incremental Approach to Improving Compliance Verification With the Biological Weapons Convention

Cropper,

Rath,

Teo

et al. 2023

Health Security

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Section: Policy Proposalsmentioning

confidence: 99%

A Modular-Incremental Approach to Improving Compliance Verification With the Biological Weapons Convention

Cropper,

Rath,

Teo

et al. 2023

Health Security

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“…Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner [10,11]. The two terms have different concepts but are related to each other [12]. Data mining is part of knowledge discovery in databases process, consisting of stages such as data selection, pre-processing, transformation, data mining, and evaluation of results [13,14].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Data Discovery (Frequent Pattern Growth): The Association Rules for Evergreen Activities on Computer Monitoring

Asrin

Saide

Ratna

et al. 2020

Advances in Intelligent Systems and Computing

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The aim of this research paper is to construct a set of guidelines that can improve the quality and efficiency of knowledge data discovery process by utilizing various types of knowledge domains. In addition, this paper offered the way of how the knowledge domain could be adopted for helping the system developer. The methodologies contain various scenarios of data exploring and the authors used data mining approach. The paper shows evidence of important to adopt data mining methods in the industry sector as well as the advantages and disadvantages. Evergreen human machine interface (HMI) at PT. Chevron Pacific Indonesia (CPI) is kind of activities to maintenance computer equipment. Nowadays, the errors were frequently happened in the accuracy of computer maintenance which has a profound effect on production results. Therefore, this study focuses on the rules of association using the frequent pattern growth algorithm (FP-growth) which is producing knowledge with trust value of 100% and a support value is 95%. The value results of support and confidence are the new approach and knowledge for the management level to decide decisions in the evergreen activities process.

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“…While data mining aims to find hidden knowledge in the data, IR intends to recover unstructured data in extensive collections according to the user's needs. In this case, the data are presented to the user in the way it is stored, which does not imply that a discovery was made (BEN-DOV;FELDMAN, 2009). However, there is a wide intersection between these areas.…”

Section: Information Retrievalmentioning

confidence: 99%

Large scale similarity-based time series mining

Silva¹

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Séries temporais são ubíquas no dia-a-dia do ser humano. Dados organizados no tempo são gerados em uma infinidade de domínios de aplicação, como medicina, biologia, economia e processamento de sinais. Devido ao grande interesse nesse tipo de dados, diversos métodos de mineração de dados temporais foram propostos nas últimas décadas. Muitos desses métodos possuem uma característica em comum: em seu núcleo, há uma função de (dis)similaridade utilizada para comparar as séries. Dynamic Time Warping (DTW) é indiscutivelmente a medida de distância mais relevante na análise de séries temporais. A principal dificuldade em se utilizar a DTW é seu alto custo computacional. Ao mesmo tempo, algumas tarefas de mineração de séries temporais, como descoberta de motifs, requerem um alto número de cálculos de distância. Essas tarefas despendem um grande tempo de execução, mesmo utilizando-se medidas de distância menos custosas, como a distância Euclidiana. Esta tese se concentra no desenvolvimento de algoritmos eficientes que permitem a análise de dados temporais em larga escala, utilizando métodos baseados em similaridade. As contribuições desta tese têm implicações em variadas tarefas de mineração de dados, como classificação, agrupamento e descoberta de padrões frequentes. Especificamente, as principais contribuições desta tese são: (i) um algoritmo para acelerar o cálculo exato da distância DTW e sua incorporação ao processo de busca por similaridade; (ii) um novo algoritmo baseado em DTW para prover invariância a prefixos e sufixos espúrios no cálculo da distância; (iii) uma representação de similaridade musical com implicações em diferentes tarefas de mineração de dados musicais e um algoritmo eficiente para computá-la; (iv) um método eficiente e anytime para encontrar motifs e discords baseado na medida DTW invariante a prefixos e sufixos.

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Text Mining and Information Extraction

Cited by 11 publications

References 56 publications

A Modular-Incremental Approach to Improving Compliance Verification With the Biological Weapons Convention

A Modular-Incremental Approach to Improving Compliance Verification With the Biological Weapons Convention

Knowledge Data Discovery (Frequent Pattern Growth): The Association Rules for Evergreen Activities on Computer Monitoring

Large scale similarity-based time series mining

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