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Techniques for knowledge discovery in existing biomedical databases: Estimation of individual aging effects in cognition in relation to dementia

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JOURNAL OF CLINICAL EPIDEMIOLOGY 2003年第2期56卷 116-123页

作者： Mitnitski, AB Mogilner, AJ Graham, JE Rockwood, K Dalhousie Univ Geriatr Med Res Unit Halifax NS B3H 2E1 Canada

New interest is being expressed in the systematic application of modeling techniques to existing datasets. Under the rubric of knowledge discovery in databases (KDD) large databases are being exploited for commercial and scientific purposes. This article reviews the development and applications of KDD techniques to dementia, using the longitudinal Canadian Study of Health and Aging dataset. KDD has demonstrated usefulness at the group level. For example, as in the course of functional impairment between Alzheimer's disease and no cognitive impairment suggest damage, control-protection mechanisms for the former compared with noncompensated random accumulation of deficits for the latter. At the individual level, KDD suggests that more precise diagnosis seems possible as well as individual life expectancy prediction. Biomedical databases appear to hold the potential for novel insights when explored by systematic modeling. (C) 2003 Elsevier Science Inc. All rights reserved.

关键词： dementia knowledge discovery in databases Bayesian statistics

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Intelligent　Information　Management　and　knowledge　discovery　in　Large　Numeric　and　Scientific　databases

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Journal of Systems Engineering and Electronics 1996年第2期7卷 73-86页

作者： Patrick Perrin Frederick E. Petry & William Thomason(Center for Intelligent and knowledge-Based Systems)(Computer Science Department, Tulane University, New Orleans LA) Center for Intelligent and Knowledge-Based Systems Computer Science Department Tulane University New Orleans LA

The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.

关键词： knowledge discovery in databases Machine learning Decision tree inducers

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Contribution to the problem of knowledge discovery of a processional character in databases 16

Contribution to the problem of knowledge discovery of a proc...

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16th International Carpathian Control Conference (ICCC)

作者： Lesso, Igor Horovcak, Pavel Flegner, Patrik Gasparova, Zuzana Tech Univ Kosice Fac BERG Inst Control & Informatizat Prod Proc Kosice Slovakia

ISBN: (纸本)9781479973705

The term KDD (knowledge discovery in databases) appeared in early 90s of the last century, due to the spread of information technology in the society and as a result of information technology development. The organization of large-scale, automated collection of data in databases, in data warehouses and data repositories enables non-trivial acquisition of implicit and potentially useful information from data. This issue is in practice mostly linked to the needs of strategic management decisions. In the present paper one of the possible approaches to knowledge discovery in long-term measurement of data of a processional character is pointed out. Authors of the paper, using a special method, evaluated the long-term measurements of the air temperature in two cities (Kosice, Prague). The temperature data were scanned online and transmitted to the database via the internet. The resulting two sequences of temperatures are appropriately reorganized into the structure of a vector space with the inner product. By a suitable transformation of data vectors from the time domain to the frequency domain and by subsequent visualization it was shown that the environment of man, in terms of thermodynamics, is a non-linear dynamic system with the characteristics of chaos. Although the knowledge gained only confirmed in an exact way a priori expectations, the method described can be used universally across the class of processes with the observed dynamics.

关键词： knowledge discovery in databases Hilbert space norm of vector visualization of the state-space trajectory

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Machine Learning-Based Approach for the Gambling Problem Identification

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VIETNAM JOURNAL OF COMPUTER SCIENCE 2025年第0期

作者： Kozak, Jan Probierz, Barbara Juszczuk, Przemyslaw Dziczkowski, Grzegorz Jach, Tomasz Stefanski, Piotr Glowania, Szymon Hrabia, Anita Wolek, Gabriel Sznapka, Wojciech Swierk, Lukasz Joniec, Natalia Univ Econ Katowice Dept Machine Learning 1 Maja 50 PL-40287 Katowice Poland STS SA Bookmaker Co Porcelanowa 8 PL-40246 Katowice Poland

Watching sports matches is a beloved pastime for every fan, and predicting the results and betting on which team will win adds to the excitement. Unfortunately, for some users, a lack of self-control can turn initially innocent play into a long-term problem. For this reason, problem gambling is among the crucial issues facing modern societies. Bookmaker companies, therefore, put considerable effort into implementing responsible gambling practices;however, identifying at-risk individuals remains a major challenge. Our paper focuses on identifying users who may currently have, or soon develop, potential issues with gambling. To achieve this, we introduce a set of machine learning methods combined with preprocessing tools that allow us to initially acquire and anonymize user data. This anonymized database is then used to identify high-risk groups. We tested our approach on a large dataset that was obtained and preprocessed specifically for this study. The experiments were conducted on actual data and verified by specialists responsible for identifying gambling problems. Using our method, we successfully identified and detected the early signs of potential gambling problems in multiple users.

关键词： Machine learning knowledge discovery in databases responsible gambling analysis of real-word data

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MiDaS: Extract Golden Results from knowledge discovery Even over Incomplete databases 22nd

MiDaS: Extract Golden Results from Knowledge Discovery Even ...

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22nd Annual International Conference on Computational Science (ICCS)

作者： Rodrigues, Lucas S. Vespa, Thiago G. Eleuterio, Igor A. R. Oliveira, Willian D. Traina, Agma J. M. Traina Jr, Caetano Univ Sao Paulo Inst Math & Comp Sci Sao Carlos Brazil

ISBN: (纸本)9783031087608;9783031087592

The continuous growth in data collection requires effective and efficient capabilities to support knowledge discovery in databases (KDD) over large amounts of complex data. However, as activities such as data acquisition, cleaning, preparation, and recording may lead to incompleteness, impairing the KDD processes, specially because most analysis methods do not adequately handle missing data. To analyze complex data, such as performing similarity search or classification tasks, KDD processes require similarity assessment. However, incompleteness can disrupt the assessment evaluation, making the system unable to compare incomplete tuples. Therefore, incompleteness can render databases useless for knowledge extraction or, at best, dramatically reducing their usefulness. In this paper, we propose MiDaS, a framework based on a RDBMS system that offers tools to deal with missing data employing several strategies, making it possible to assess similarity over complex data, even in the presence of missing data at KDD scenarios. We show experimental results of analyses using MiDaS for similarity retrieval, classification, and clustering tasks over publicly available complex datasets, evaluating the quality and performance of several missing data treatments. The results highlight that MiDaS is well-suited for dealing with incompleteness enhancing data analysis in several KDD scenarios.

关键词： knowledge discovery in databases Missing data RDBMS framework Similarity queries

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knowledge discovery in time series databases

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2001年第1期31卷 160-169页

作者： Last, M Klein, Y Kandel, A Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA Tel Aviv Univ Dept Elect Engn Syst IL-69978 Tel Aviv Israel

Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDB includes cleaning and filtering of time series data, identifying the most important predicting, attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques and the information-theoretic fuzzy approach to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation. We demonstrate our approach an two types of time series: stock-market data and weather data.

关键词： computational theory of perception data mining fuzzy association rules knowledge discovery in databases time series databases

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Review of Maturity Models for Data Mining and Proposal of a Data Preparation Maturity Model Prototype for Data Mining

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COMPUTERS 2025年第4期14卷 146-146页

作者： Hochkamp, Florian Scheidler, Anne Antonia Rabe, Markus Tech Univ Dortmund Dept IT Prod & Logist D-44227 Dortmund Germany

Companies face uncertainties evaluating their own capabilities when implementing data analytics or data mining. Data mining is a valuable process used to analyze data and support decisions based on the knowledge generated, creating a pipeline from the phases of data collection to data preparation and data mining. In order to assess the target state of a company in terms of its data mining capabilities, maturity models are viable tools. However, no maturity models exist that focus on the data mining process and its particular phases. This article discusses existing maturity models in the broader field of data mining, such as data management, machine learning, or artificial intelligence. Focusing on the most cost-relevant phase of data preparation, the critical influences for the development of a maturity model for the phase of data preparation are identified and categorized. Additionally, a data preparation maturity model prototype is proposed. This provides the first step towards the design of a maturity model for data mining.

关键词： maturity models process models knowledge discovery in databases data pre-processing data mining machine learning artificial intelligence

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DATA-DRIVEN discovery OF QUANTITATIVE RULES IN RELATIONAL databases

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 1993年第1期5卷 29-40页

作者： HAN, JW CAI, YD CERCONE, N School of Computing Science Simon Fraser University Burnaby BC Canada

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. In this paper, an efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. Our method learns both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. Our paper shows that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases.

关键词： knowledge discovery in databases MACHINE LEARNING ATTRIBUTE-ORIENTED INDUCTION QUANTITATIVE RULES CHARACTERISTIC RULES CLASSIFICATION RULES DATA-DRIVEN LEARNING ALGORITHMS

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A hierarchical model learning approach for refining and managing concept clusters discovered from databases

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DATA & knowledge ENGINEERING 1996年第2期20卷 227-252页

作者： Zhong, N Ohsuga, S WASEDA UNIV SCH SCI & ENGN DEPT INFORMAT & COMP SCI SHINJUKU KU TOKYO 169 JAPAN

The contents of most databases are ever-changing, and erroneous data can be a significant problem in real-world databases. Therefore, the process of discovering knowledge from databases is a process based on incipient hypothesis generation/evaluation and refinement/management. Although many systems for knowledge discovery in databases have been proposed, most systems have not addressed the capabilities of refining/managing the discovered knowledge. This paper describes a hierarchical model learning approach for refining/managing concept clusters discovered from databases. This approach is the basic one for developing HML (Hierarchical Model Learning), which is one sub-system of our GLS (Global Learning Scheme) discovery system and can be cooperatively used with other sub-systems of GLS such as DBI (Decomposition Based Induction). By means of HML, concept clusters discovered from a database by DBI can be represented as the Multi-Layer Logic formulae with hierarchical models in a knowledge-base and can be easily refined/managed according to data change in a database and/or domain knowledge. HML is based on the model representation of Multi-Layer Logic (MLL). Its key feature is the quantitative evaluation for selecting the best representation of the MLL formulae by using cooperatively a criterion based on information theory and domain knowledge. Experience with a prototype of HML implemented by the knowledge-based system KAUS is discussed.

关键词： knowledge discovery in databases multi-layer logic machine learning information theory hierarchical modeling refinement management

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Mining very large databases

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COMPUTER 1999年第8期32卷 38-+页

作者： Ganti, V Gehrke, J Ramakrishnan, R Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

The explosive growth of databases makes the scalability of data-mining techniques increasingly important. The authors describe algorithms that address three classical data mining problems.

关键词： classification clustering data analysis data analysis data mining data mining algorithms data mining techniques input data input database knowledge discovery in databases main memory market basket analysis pattern classification pattern clustering scalable algorithms strategic decision making vast data stores very large data sets very large database mining very large databases

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