Mining association rules from imprecise ordinal data

Chen, Yen-Liang; Weng, Cheng-Hsiung

doi:10.1016/j.fss.2007.10.005

Cited by 31 publications

(11 citation statements)

References 32 publications

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“…Therefore, from the algorithm's point of view, these two categories can be merged into a single category. Finally, the third category extends the algorithms so that they can find association rules from quantitative data, such as salary, height, humidity, and so on (Chen & Weng, 2008). According to Agrawal & Sirkant (1994) ∪ .…”

Section: Data Mining Association Rulesmentioning

confidence: 99%

On Ranking Discovered Rules of Data Mining by Data Envelopment Analysis: Some Models with Wider Applications

Toloo¹,

Nalchigar²

2011

New Fundamental Technologies in Data Mining

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Section: Data Mining Association Rulesmentioning

confidence: 99%

On Ranking Discovered Rules of Data Mining by Data Envelopment Analysis: Some Models with Wider Applications

Toloo¹,

Nalchigar²

2011

New Fundamental Technologies in Data Mining

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Section: Data Mining Association Rulesmentioning

confidence: 99%

“…Therefore, the importance of data mining is becoming increasingly obvious. Many data mining techniques have also been presented in various applications, such as association rule mining, sequential pattern mining, classification, clustering, and other statistical methods (Chen & Weng, 2008). Association rule mining is a widely recognized data mining method that determines consumer purchasing patterns in transaction databases.…”

Section: Introductionmentioning

confidence: 99%

New Fundamental Technologies in Data Mining

Funatsu¹

2011

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Prof. Kimito Funatsu was born on December 3, 1955 in Japan. He received his Doctor degree (Dr. Sci.; physical organic chemistry) from Kyushu University which is one of the oldest imperial universities (1983) and joined Prof. Shin-ichi Sasaki's group, Toyohashi University of Technology (1984) as research-associate. During researching in Prof. Sasaki's group he worked on chemoinformatics field, i.e., Structure elucidation system CHEMICS, organic synthesis design system AIPHOS, molecular design. In 1990 he took the post of associate-professor. In 2004 he moved to The University of Tokyo as full Professor to continue the same research work. Recently he is trying material design and soft sensor. He is a chair of division of chemical information and computer sciences, Chemical Society of Japan.Dr. Kiyoshi Hasegawa was born on August 18, 1963 in Kanazawa, Japan. He received his Ph.D. from Toyohashi University of Technology (1995) and worked at Biostructure group in Chugai Pharmaceutical Company in Japan. While working in Chugai, he spent three months at Swiss Basel Research Center in Roche for developing new de novo design program (2003). He is now principal scientist in his group and is involved in many SBDD projects. He is also the Roche data mining working group member and the secretary general of Japan Chemical Information Science. His interests are de novo design, Chemogenomics, data mining and web application.

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“…Looking at Table 1, the pattern {T reatment1 = Average 1 } is a fraction of the opinion expressed by the practitioner P 1 and therefore the extracted information is not complete. Unfortunately, this type of output is generated by uncertain mining approaches [7,8,9]. An opinion pattern would be T reatment 1 = Bad 0.3 Average 0.7 and is considered as frequent since it does not contradict with the opinion of P 2 .…”

Section: Introductionmentioning

confidence: 99%

Expert Opinion Extraction from a Biomedical Database

Samet

Guyet

Négrevergne

et al. 2017

Lecture Notes in Computer Science

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Abstract. In this paper, we tackle the problem of extracting frequent opinions from uncertain databases. We introduce the foundation of an opinion mining approach with the definition of pattern and support measure. The support measure is derived from the commitment definition. A new algorithm called OpMiner that extracts the set of frequent opinions modelled as a mass functions is detailed. Finally, we apply our approach on a real-world biomedical database that stores opinions of experts to evaluate the reliability level of biomedical data. Performance analysis showed a better quality patterns for our proposed model in comparison with literature-based methods.

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Mining association rules from imprecise ordinal data

Cited by 31 publications

References 32 publications

On Ranking Discovered Rules of Data Mining by Data Envelopment Analysis: Some Models with Wider Applications

On Ranking Discovered Rules of Data Mining by Data Envelopment Analysis: Some Models with Wider Applications

New Fundamental Technologies in Data Mining

Expert Opinion Extraction from a Biomedical Database

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