Logic of Association Rules

Rauch, Jan

doi:10.1023/b:apin.0000047380.15356.7a

Cited by 60 publications

(38 citation statements)

References 5 publications

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“…ARs are produced by humans, although the use of natural language generation was also studied [18]. Prior to entering the reports, the sets of discovered hypotheses (understood as formulae in the socalled observational calculus) can be transformed using formal deduction rules [15,16] into 'canonical form' (which is, among other, free of redundancies). In the cardiovascular risk application, a collection of ARs has been created by junior researchers based upon results of selected 4ft-Miner tasks on STULONG data.…”

Section: Results Deployment Over Semantic Webmentioning

confidence: 99%

Ontology-Enhanced Association Mining

Svátek

Rauch

Ralbovský

2006

Semantics, Web and Mining

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Abstract. The roles of ontologies in KDD are potentially manifold. We track them through different phases of the KDD process, from data understanding through task setting to mining result interpretation and sharing over the semantic web. The underlying KDD paradigm is association mining tailored to our 4ft-Miner tool. Experience from two different application domains-medicine and sociology-is presented throughout the paper.

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Section: Results Deployment Over Semantic Webmentioning

confidence: 99%

Ontology-Enhanced Association Mining

Svátek

Rauch

Ralbovský

2006

Semantics, Web and Mining

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“…The task of association rule mining is to find the rule A  B which the support and confidence in transaction database D satisfy the min_sup and the min_con specified by the users, that is strong association rules. Association rules mining is a two step process: (1) finding all frequent item sets; (2) generating strong association rules by frequent item sets [7] . Apriori algorithm is a classic frequent item sets mining algorithm.…”

Section: Association Rules and Improvement Of Apriori Algorithmmentioning

confidence: 99%

Research on intelligent recommendation algorithm of e-commerce based on association rules

Shen

Cheng

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Corresponding author:xycheng@ntu.com Abstract. As the commodities of e-commerce are more and more rich, more and more consumers are willing to choose online shopping, because of these rich varieties of commodity information, customers will often appear aesthetic fatigue. Therefore, we need a recommendation algorithm according to the recent behavior of customers including browsing and consuming to predicate and intelligently recommend goods which the customers need, thus to improve the satisfaction of customers and to increase the profit of e-commerce. This paper first discusses recommendation algorithm, then improves Apriori. Finally, using R language realizes a recommendation algorithm of commodities. The result shows that this algorithm provides a certain decision-making role for customers to buy commodities.

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“…Background Association Rules are GUHA-like association rules [36], but they are defined over meta-fields rather than over data fields or derived fields. A more formal definition is in [26].…”

Section: Background Knowledge: Patternsmentioning

confidence: 99%

“…A more formal definition is in [26]. A Mutual Influence can be transformed into Background Association Rules (BAR) of the form A(ω A ) ≈ B(ω B ), where A(ω A ) and B(ω B ) are Basic Boolean Meta-attributes [36]. The coefficient ω X is a subset of values of meta-field X that are perceived as 'high'.…”

Section: Background Knowledge: Patternsmentioning

confidence: 99%

SEWEBAR-CMS: semantic analytical report authoring for data mining results

et al. 2010

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SEWEBAR-CMS is a set of extensions for the Joomla! Content Management System (CMS) that extends it with functionality required to serve as a communication platform between the data analyst, domain expert and the report user. SEWEBAR-CMS integrates with existing data mining software through PMML. Background knowledge is entered via a web-based elicitation interface and is preserved in documents conforming to the proposed Background Knowledge Exchange Format (BKEF) specification. SEWEBAR-CMS offers web service integration with semantic knowledge bases, into which PMML and BKEF data are stored. Combining domain knowledge and mining model visualizations with results of queries against the knowledge base, the data analyst conveys the results of the mining through a semiautomatically generated textual analytical report to the end user. The paper demonstrates the use of SEWEBAR-CMS on a real-world task from the cardiological domain and presents a user study showing that the proposed report authoring support leads to a statistically significant decrease in the time needed to author the analytical report.

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Logic of Association Rules

Cited by 60 publications

References 5 publications

Ontology-Enhanced Association Mining

Ontology-Enhanced Association Mining

Research on intelligent recommendation algorithm of e-commerce based on association rules

SEWEBAR-CMS: semantic analytical report authoring for data mining results

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