Top Research Papers on Data Mining

O objetivo do estudo é analisar, sob o enfoque da mineração de dados, as informações do Relato Integrado de Gestão (RIG) em algumas Unidades Prestadoras de Contas (UPCs) brasileiras por meio da ferramenta Orange Data Mining (ODM). Para tanto, foi realizado um estudo qualitativo, documental e exploratório por meio de práticas de análise textual de dados financeiros e não financeiros do RIG de quinze universidades federais brasileiras. São apresentados dois exemplos de análises, com foco em um único exercício, 2019, o que poderá ser mais explorado em estudos futuros, considerando-se a expectativ...

Free open source software like Keel, KNIME, RapidMiner, Weka, Tanagra, and Orange are explained and compared and the free open source and the proprietary data mining tool is compared.

This paper was written to review existing papers regarding data mining, especially classification, to get information and map from research that has been done to be used as literature on the author's research plan.

This research includes the relationship between these two mining process that facilitated further elucidation of the wisdom mining process and proposed improvements to data mining techniques and their applications in the real world and emphasised the need to seek ways to harness wisdom from data.

This book covers association mining, classification, mobile marketing, opinion mining, microarray data mining, internet mining and applications of data mining on biological data, telecommunication and distributed databases, among others, while promoting understanding and implementation of data mining techniques in emerging domains.

The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model and shows that SVM has the highest accuracy in comparison to other algorithms.

Data abundance raises the precision of the best predictors, but it can induce data miners to search less intensively for high‐precision signals, and can therefore reduce asset managers' average performance.

Considering recent advancements in software projects for DM, intelligent data control system design and specifications are proposed as an example of DM application in official data processing.

Various data mining techniques that help to identify patterns and relationships to help make business decisions using data analysis are discussed which deals with joint analysis of multiple inter-related datasets providing multiple complementary views to help further with precise decision-making.

Data and web mining involves the use of data mining techniques to extract information from the web in order to gain insights into online behavior, customer preferences, and trends, which includes improved decision-making and business growth.

Traditionally, the mining sector has been considered a slow innovator compared to other industries, like the manufacturing or pharmaceutical industries. However, we observe an upsurge in the innovation activity of the mining industry in the first half of the 2000s. During this period, mining innovation started to increase rapidly after periods of stagnation and downward trends. To conduct and in-depth investigation of the global trends and patterns behind this structural change in mining innovation, we formulated a general search strategy to identify patent activity in this sector. The strateg...

An overview of the data mining process, aswell as its benefits and drawbacks, as well as data mining methodologies and tasks are provided.

This article uses SPSS Clementine 12.0 to construct a “financial early warning intelligent system” and establishes a set of financial analysis model that uses clustering, association rules, and decision tree methods for joint analysis.

An extent review and summary of Big Data Mining techniques with the most common data mining algorithms suitable to be used to handle large datasets and the general pros and cons of these algorithms and the correspondingappropriate fields that apply.

Zusammenfassung Ein datengetriebener Forschungsansatz entspringt dem Ursprungsgedanken der Homöopathie, der mit modernen statistischen Konzepten in das 21. Jahrhundert überführt werden und zur Weiterentwicklung einer wissenschaftlichen Identität beitragen kann. Eine Agenda zur Methodenforschung, die auf einer vollständig verschlagworteten Materia medica basiert, wird vorgestellt.

Web Mining is part of data mining technology, which aims to extract interesting and useful hidden patterns and information from web documents and web activities.

This research presents a meta-modelling architecture that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging individual pieces of data to provide insights about their owners.

This paper overviews two interdependent issues important for mining remote sensing data obtained from atmospheric monitoring missions to investigate deep learning methodologies for atmospheric data classification based on vast amount of data and without ground truth or with very limited ground truth.

The relationship between research and practical use and what can be done from an academic standpoint is discussed and the results presented by researchers are difficult for a general person to understand.

This paper provides a more up-to-date survey of spatiotemporal data mining methods and has a detailed survey of parallel formulations of spatio-spatiotem temporal data mining.

Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems.

The SmartSHARK repository mining data is a collection of rich and detailed information about the evolution of software projects that enables us to explore research questions that require data from different sources and/or longitudinal data over time.

Experimental analysis indicates that the proposed work is more successful in terms of attack resistance, scalability, execution speed and accuracy when compared with other algorithms that are used for privacy preservation.

This paper has analyzed the use of Deep Learning (DL) and Machine Learning (ML) algorithms for the detection and classification of malicious URLs for the detection and classification of suck attacks.

Although, the process analytics pipeline from raw video data to a discovered process model has not yet been fully implemented, it is convinced that the approach is an essential contribution towards a (semi)automatic technique aiming to replace manual work.

How data mining technology can support educational decision-making, improve teaching effectiveness and learning experience is expounded, providing new ideas for realizing intelligent and personalized education.

According to the experimental results, the highest accuracy is k-NN has of 84 % when compared to the NB, which implies that k-NN could be useful for accurate soil type classification in the agricultural domain.

The heated debate regarding not only its value in the public safety community but also whether data mining reflects an ethical, or even legal, approach to the analysis of crime and intelligence data is confounding the question of whether to acquire data mining technology.

The method comprises setting data mining workflow, said workflow comprising a plurality of parallel data processing task, and activating the workflow, and said plurality of when parallel dataprocessing task is triggered can improve the efficiency of data mining.

A novel taxonomy of the application of federated learning in data mining is provided and four promising research directions for further research are discussed, that is, privacy enhancement, improvement of communication efficiency, heterogeneous system processing, and reducing economic costs.

It is argued that SQL-92 plus recursive tables is capable of expressing user-defined algorithms, and selected algorithms out of graph mining, clustering and association rule analysis are transformed into recursive common table expressions (CTEs).

This work provides a generic review of the existing data mining ontologies (DMOs) and also provides a base platform for ontology developers and researchers for gauging the ontologies for satisfactory coverage and usage.

This paper focuses on scalable mining of huge volumes of temporal coronavirus disease 2019 (COVID-19) data at different granularity levels, and finds implicit, previously unknown and potentially useful information and knowledge which can be discovered by data mining for social good.

Non-centrosymmetric molecular crystals are effective in nonlinear optical applications, such as second-harmonic generation, and optical rectification, due to low dielectric constants and high molecular hyperpolarizabilities. We designed a combined method of data mining for non-centrosymmetric structure from the Cambridge Structural Database (CSD) and performing density functional theory calculations to discover new organic nonlinear optical crystals that generate intense terahertz (THz) radiation. To confirm our combination approach, we recrystallized and tested the newly discovered organic no...

This article analyzes extensive data libraries and how they benefit when completed and aims to increase discussion between readers and librarians about using Big Data, especially in libraries.

In this paper, various methods are being proposed on how to find the person is having diabetes using A diabetes dataset by implementing various machine-learning algorithms.

According to the theorem that the cell complex reaches the optimum when it has the smallest possible critical point, this study applies the concept of critical points in the discrete Morse theory to optimize the grid clustering process to obtain clustering results.

The SmartSHARK repository mining data is a collection of rich and detailed information about the evolution of software projects that enables to explore research questions that require data from different sources and/or longitudinal data over time.

The problem solved by data mining techniques in different areas to improve the success of students is addressed and the most important studies conducted to date in this area are discussed.

The goal of this paper is to assist patient data scientists in obtaining a clear and straightforward comprehension of how to use clinical datamining technology to promote the production of research results that benefit doctors and patients.

This ontology deals with Indian court cases on intellectual property rights (IPR) and aims to organise the legal information in a way that is useful for practitioners and downstream automation tasks.

A brief introduction of data analytics, architecture of big data, knowledge discovery and big data algorithms is presented.

The characteristics of today’s Internet data from the background of big data, and the main method of data scraping-crawlers are introduced.

The aim of this research was improving lymphoma diagnosis accuracy of machine learning methods with Min Max Scalar normalisation techniques.

The clustering algorithm of buyers’ purchasing behavior is used as an example to illustrate the definition of VMML in the clustering analysis of purchasing behavior, and an e-commerce research data warehouse is constructed through the integration of data.

The research issues as well as challenges of stream data mining and likewise huge data-oriented flow data mining are discussed, including the need for privacy protection in data mining.

This paper analyzes the challenges involved in designing data mining techniques for mining data streams besides evaluating various existing techniques and their preprocessing methods to reveal which methods are feasible and which methods are not feasible in real-time data streaming applications.

This review research focuses on and quickly discusses the various domains in which data mining is applied and the numerous technologies used in related fields, which depend on user desire.

The results of the study demonstrate the high performance of the models in soil sample classification tasks, highlighting their potential for improving soil resource management and increasing crop yields.

The role of data mining methods in cyber security, which is being used to deliver solutions such as intrusion detection and auditing, is discussed.