Relevant bibliographies by topics / Data mining in relational databases

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Academic literature on the topic 'Data mining in relational databases'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Dissertations / Theses on the topic "Data mining in relational databases"

1

Chen, Yu 1979. "Data mining relational databases with probabilistic relational models." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97928.

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Relational databases are a popular method for organizing and storing data. Unfortunately, many machine-learning techniques are unable to handle complex relational models. The Probabilistic Relational Model (PRM) is an extension of the Bayesian Network framework that can express relational structure as well as probabilistic dependencies. In this thesis, we significantly expand and improve an implementation of PRMs that allows defining conditional probability distributions over discrete and continuous variables. The thesis uses as starting point an implementation that has various problems, and r
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2

Toprak, Serkan. "Data Mining For Rule Discovery In Relational Databases." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605356/index.pdf.

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Data is mostly stored in relational databases today. However, most data mining algorithms are not capable of working on data stored in relational databases directly. Instead they require a preprocessing step for transforming relational data into algorithm specified form. Moreover, several data mining algorithms provide solutions for single relations only. Therefore, valuable hidden knowledge involving multiple relations remains undiscovered. In this thesis, an implementation is developed for discovering multi-relational association rules in relational databases. The implementation is based on
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3

Jafer, Yasser. "Aggregation and Privacy in Multi-Relational Databases." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22695.

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Most existing data mining approaches perform data mining tasks on a single data table. However, increasingly, data repositories such as financial data and medical records, amongst others, are stored in relational databases. The inability of applying traditional data mining techniques directly on such relational database thus poses a serious challenge. To address this issue, a number of researchers convert a relational database into one or more flat files and then apply traditional data mining algorithms. The above-mentioned process of transforming a relational database into one or more flat fi
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4

Zou, Beibei 1974. "Data mining with relational database management systems." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82456.

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With the increasing demands of transforming raw data into information and knowledge, data mining becomes an important field to the discovery of useful information and hidden patterns in huge datasets. Both machine learning and database research have made major contributions to the field of data mining. However, there is still little effort made to improve the scalability of algorithms applied in data raining tasks. Scalability is crucial for data mining algorithms, since they have to handle large datasets quite often. In this thesis we take a step in this direction by extending a popula
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5

Ma, Xuesong 1975. "Data mining using relational database management system." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98757.

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With the wide availability of huge amounts of data and the imminent demands to transform the raw data into useful information and knowledge, data mining has become an important research field both in the database area and the machine learning areas. Data mining is defined as the process to solve problems by analyzing data already present in the database and discovering knowledge in the data. Database systems provide efficient data storage, fast access structures and a wide variety of indexing methods to speed up data retrieval. Machine learning provides theory support for most of the popular d
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6

Kanodia, Juveria. "Structural advances for pattern discovery in multi-relational databases /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/978.

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Daglar, Toprak Seda. "A New Hybrid Multi-relational Data Mining Technique." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606150/index.pdf.

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Multi-relational learning has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. As patterns involve multiple relations, the search space of possible hypotheses becomes intractably complex. Many relational knowledge discovery systems have been developed employing various search strategies, search heuristics and pattern language limitations in order to cope with the complexity of hypothesis space. In this work, we propose a relational concept learning technique, which adopts concept descriptio
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8

Collopy, Ethan Richard. "Data mining temporal and indefinite relations with numerical dependencies." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327099.

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9

Lepinioti, Konstantina. "Data mining and database systems : integrating conceptual clustering with a relational database management system." Thesis, Bournemouth University, 2011. http://eprints.bournemouth.ac.uk/17765/.

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Many clustering algorithms have been developed and improved over the years to cater for large scale data clustering. However, much of this work has been in developing numeric based algorithms that use efficient summarisations to scale to large data sets. There is a growing need for scalable categorical clustering algorithms as, although numeric based algorithms can be adapted to categorical data, they do not always produce good results. This thesis presents a categorical conceptual clustering algorithm that can scale to large data sets using appropriate data summarisations. Data mining is dist
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10

Oyama, Fernando Takeshi [UNESP]. "Mineração multirrelacional de regras de associação em grandes bases de dados." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/98694.

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Made available in DSpace on 2014-06-11T19:29:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-02-22Bitstream added on 2014-06-13T20:39:07Z : No. of bitstreams: 1 oyama_ft_me_sjrp.pdf: 1107324 bytes, checksum: 0977db2af1589dece4aa46b5882d84d6 (MD5)<br>O crescente avanço e a disponibilidade de recursos computacionais viabilizam o armazenamento e a manipulação de grandes bases de dados. As técnicas típicas de mineração de dados possibilitam a extração de padrões desde que os dados estejam armazenados em uma única tabela. A mineração de dados multirrelacional, por sua vez, apresenta-se
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