Data Mining of Association Rules and the Process of Knowledge Discovery in Databases

Hipp, Jochen; Güntzer, Ulrich; Nakhaeizadeh, Gholamreza

doi:10.1007/3-540-46131-0_2

Cited by 17 publications

(19 citation statements)

References 15 publications

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“…We report our experimental results on the performance of DCAR in comparison with the algorithm DQMWAR [7] , and the method, which cleans the data using the business rules offered by experts.…”

Section: Methodsmentioning

confidence: 99%

“…Basing on the basic idea of DMQ mentioned in document [7], a data cleaning method based on association rules is proposed. The method based on the association rules mined from the sample data, makes explore in integrating the association rules into the advanced business rules.…”

Section: Basic Ideamentioning

confidence: 99%

“…The method proposed in document [7] offers a new thought to solve this problem. The method detects, quantifies and corrects data though the association rules mined from the data themselves.…”

Section: Introductionmentioning

confidence: 99%

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A Data Cleaning Method Based on Association Rules

Wei-Jie¹,

Zhang²,

Zhang³

et al. 2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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The quality of the data affects the usability of the data mining's results. Making a data preparation before the mining can improve the quality. If the data are collected from the multi-data source, data preparation becomes very difficult. In this paper, a data-cleaning method based on the association rules is proposed. The new method adjusts the basic business rules provided by the experts with association rules mined from multi-data sources, and generates the advanced business rules for every data source. Using this method, time is saved and the accuracy of the data cleaning is improved.

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Section: Methodsmentioning

confidence: 99%

Section: Basic Ideamentioning

confidence: 99%

See 1 more Smart Citation

A Data Cleaning Method Based on Association Rules

Wei-Jie¹,

Zhang²,

Zhang³

et al. 2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

View full text Add to dashboard Cite

show abstract

“…Essa tarefaé foco deste trabalho e tem recebido grande atenção, seja naárea acadêmica ou em aplicações práticas, em função de sua aplicabilidade a problemas de negócio (Antonie & Zaane, 2004;Oliveira, Zaane, & Saygin, 2004;Besemann, Denton, Yekkirala, Hutchison, & Anderson, 2004;Aggelis, 2004;Hipp, Güntzer, & Nakhaeizadeh, 2002).…”

Section: Mineração De Dados E Associaçãounclassified

“…Figura 2.2: Espaço de busca para o conjunto de itens A = {a, b, c, d, e} (Hipp, Güntzer, & Nakhaeizadeh, 2002) Para que não haja necessidade de percorrer todo o espaço de buscaà procura dos itemsets de fato freqüentes, os algoritmos atuais para minerar regras de associação utilizam um método que gera e testa itemsets candidatos. Esses algoritmos geram conjuntos de itemsets potencialmente freqüentes chamados conjuntos de itemsets candidatos e utilizam-se da propriedade de linha de fronteira (downward closure) do suporte de um itemset (Agrawal & Srikant, 1994), que determina que todo subconjunto de um itemset freqüente deve ser freqüente (para remover os itemsets com pelo menos um subconjunto de itens não-freqüentes).…”

Section: Geração De Itemsets Freqüentesunclassified