Ontology-based data mining approach implemented on exploring product and brand spectrum

Liao, Shu-Hsien; Ho, Hsu-hui; Yang, Feng-chich

doi:10.1016/j.eswa.2009.04.030

Cited by 16 publications

(8 citation statements)

References 18 publications

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“…Mossavi et al [Mossavi et al, 2009] apply an ontological technique to determine complement products and the OWL language to model types of products. In this direction, Liao et al [2009] have updated the marketing concept of branding, which is traditionally used in the business field for making a brand by means of differentiating the brand products from those of the competitors [Baker, 1996]. A brand is "a combination of features (what the product is), customer benefits (what needs and wants the product meets) and values (what the customer associates with the product)".…”

Section: Semantic Web Miningmentioning

confidence: 98%

“…Most of them include a set of concepts hierarchically organized and additional relations between them. This formalization allows integrating heterogeneous information as a preprocessing step of web mining tasks [Liao et al, 2009].…”

Section: Semantic Web Miningmentioning

confidence: 99%

“…A brand is "a combination of features (what the product is), customer benefits (what needs and wants the product meets) and values (what the customer associates with the product)". Liao et al [2009] analyze a specific market segment by means of brand spectrums depicted from data relating to consumer purchase behaviors and beverage products. They develop a set of ontologies for describing the integrated consumer behavior and a set of databases related to these ontologies.…”

Section: Semantic Web Miningmentioning

confidence: 99%

See 2 more Smart Citations

Semantic Web Mining for Book Recommendation

Asjana

López

Muñoz

et al. 2012

Advances in Intelligent Systems and Computing

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Abstract.A current strategy for improving sales as well as customer satisfaction in the e-commerce field is to provide product recommendation to users. The increasing acceptance of web recommender systems is mainly due to the advances achieved in the intensive research carried out for several years. However, in spite of these improvements, recommender systems still present some important drawbacks that prevent from satisfying entirely their users. In this work, a methodology that combines an association rule mining method with the definition of a domainspecific ontology is proposed in order make efficient book recommendations.

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Section: Semantic Web Miningmentioning

confidence: 98%

Section: Semantic Web Miningmentioning

confidence: 99%

Section: Semantic Web Miningmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Web Mining for Book Recommendation

Asjana

López

Muñoz

et al. 2012

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

show abstract

“…These researches, however, concentrated on the design of the algorithms, yet discussion of ontology structure design and its benefit to data mining were not covered. Until recently, research on applying ontology to data mining was exploited by several studies such as, ontology-based induction of rules (Aronis et al, 1996;Taylor et al, 1997), ontology-based business understanding (Sharma & Osei-Bryson, 2009), ontologybased post-processing and explanation of association rules (Domingues & Rezende, 2005;Liao et al, 2009;Marinica et al, 2008;Svatek et al, 2005), ontology-supported selection of classification algorithms (Bernstein et al, 2005;Lin et al, 2006), ontology-guided new attributes generation from databases (Phillips & Buchanan, 2001), and ontology-based integration and preprocessing of data (Euler & Scholz, 2004;Perez-Rey et al, 2006).…”

Section: Related Workmentioning

confidence: 99%

Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

Lin

Jiang

2011

Expert Systems with Applications

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“…30 32 The statistical information is used to compute the empirical probability and the entropy is then computed using [34][35][36] ;…”

Section: Composition Of the Metrics Valuesmentioning

confidence: 99%

Using Artificial Intelligence Techniques to Implement a Multifactor Authentication System

Phiri¹,

Zhao²,

Zhu³

et al. 2011

IJCIS

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The recent years have seen a rise in the number of cases of cyber-crime committed through identity theft and fraud. To address this problem, this paper uses adaptive neural-fuzzy inference system, fuzzy logic and artificial neural network to implement a multifactor authentication system through a technique of information fusion. To begin with, the identity attributes are mined using the three corpora from three major sources namely the social networks, a set of questionnaires and application forms from the various services offered both in the real and cyberspace. The statistical information generated by the corpora is then used to compose an identity attribute metric model. The composed identity attributes metrics values classified as biometrics, device metrics and pseudo metrics are then fused at the score level through a technique of information fusion in a multifactor authentication system by using each of the above artificial intelligence technologies and the results compared.

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Ontology-based data mining approach implemented on exploring product and brand spectrum

Cited by 16 publications

References 18 publications

Semantic Web Mining for Book Recommendation

Semantic Web Mining for Book Recommendation

Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

Using Artificial Intelligence Techniques to Implement a Multifactor Authentication System

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