A new customized document categorization scheme using rough membership

Singh, Shailendra; Dey, Lipika

doi:10.1016/j.asoc.2004.08.007

Cited by 17 publications

(3 citation statements)

References 19 publications

(23 reference statements)

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“…Middleton et al(2004) developed a k-NN-based recommender system that recommends research documentation based on similar users' preferences and uses Ontology to analyze the profiles of users, and "VISCORS' a wall paper recommending system in mobile web coming collaborative filtering with content-based image retrieval was developed by Kim et al (2004). Singh and Dey (2005) developed a document ranking system based on users' preferences using a filtering agent. Cao and Li (2007) developed fuzzy-based system for recommendation of product optimized based on customers' needs extracted using interaction between systems, and a personalized eLearning system based on contextualizing multimedia systems suggested in Eze et al(2007) and a personalized tourism services aim at helping the user finding what they are looking for, easily without spending time and effort described in Kabasi (2010).These system have been classified into two types: ones that develop and test new recommendation methods, and the others that investigate empirically the factors affecting the usefulness of recommendation systems, or the effects of using recommendation systems on consumer purchasing processes (Ahn et al, 2010).…”

Section: Personalization Of Servicesmentioning

confidence: 99%

Business Intelligence Through Personalised Location-Aware Service Delivery

Ishaya¹

2012

Advances in Customer Relationship Management

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Advances in mobile communication and location-based technologies have presented business and decision-makers (such as marketing managers) with a new paradigm for Business Intelligence (BI). It has created a channel for location-aware advertising-defined as targeted advertising initiatives delivered to a mobile device from an identified service provider that is specific to the location of the consumer (Unni & Harmon, 2007). With the increasing popularity of the new generation of Global Positioning Systems (GPS)-enabled smartphones (Bellavista et al, 2008) and their ubiquity, marketers and other service providers are able to utilize this emerging technologies to deliver targeted, tailored (Gauntt, 2008) and personalized services (such advertising) based on consumers' geographical locations (W3C, 2009) and prediction of their needs (Barnes, S. & Scornavacca, 2004), and to reach them through their mobile devices on a geographically targeted basis. As a result, there are now a number of location-aware services that have been classified as-Information and navigations services, emergency assistance, tracking services and network related services (Al-Bayari & Sadoun, 2007). Location aware services means that the application is aware of the current location and can use this information to present, retrieve or filter the information appropriate to the user at a particular position. For example, current offers at restaurants that are within 10 metres could be shown to a user that is out for a night meal with friends and the device can guide them to the destination. Location-Aware Service (from now on referred to as LAS) revenues are expected to increase to about $19 billion by the year 2014 (Kobsa, 2007). Despite its ubiquity and growing popularity, LAS is yet to be fully utilized from BI perspectives for a number of reasons-one of which is users/consumers resistance /unwilling to accept this new pervasive and intrusive means of service delivery. Whilst there are limitations and concerns over indoor location technology and a fragmented location ecosystem, another impending factor is privacy-related user acceptance (Kobsa, 2007) and security/trust related issues. The potential intrusion of privacy is an important concern for users of location-aware services (Kobsa, 2007; Soroa-Joury &Yang, 2009). However, there is a clear presuposition that users with different profiles using different access networks and mobile devices require personalized services that meet their needs at specific locations. Therefore, it is important to investigate how users are responding and how BI can be properly utilized for effective location-aware customer relation management.

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Section: Personalization Of Servicesmentioning

confidence: 99%

Business Intelligence Through Personalised Location-Aware Service Delivery

Ishaya¹

2012

Advances in Customer Relationship Management

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“…Computer-aided linguistics has achieved a variety of text information transfer via current electronic network, such as for language translation, topic mining, and corpus classification (Singh and Dey 2005;Lee et al 2007). Some of them contribute to human-machine interface in the field of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Stereoscopic story visualization in literary works demonstrated by Shakespeare’s plays

Yamada

Murai

2010

J Vis

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A stereoscopic method of identifying story patterns in literary works is newly developed. The pattern is extracted from textual information by the detection of thematically assigned keywords, and depicted as visual imageries. The applicability of the method is demonstrated in several of Shakespeare's plays. The complex scenario patterns in Shakespeare's tragedies are successfully captured with applying the method for two different themes in each play. As the result, the organization of story accompanying multiple themes in a single play has been obtained as a pair of visual imageries, i.e. stereoscopic story visualization. This approach, in combination with a quadrant analysis of the plots, allows us in interpretation further complexity of human psychology in the characters and scene-by-scene transitions in each play.

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“…Rough membership (RM) [3] is the main and core measure in RST, and there are some studies, such as [4,5] using RM to make the model construction and document categorization. Thus, we will construct a new algorithm (LBRM Algorithm) for rule extraction based on RM, and the information integration is utilized while some new thoughts on discretization and clearness are provided.…”

Section: Introductionmentioning

confidence: 99%

LBRM Algorithm for Rule Extraction Based on Rough Membership

Zhang

Miao

2013

AMR

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Rule extraction is a main goal for rough set theory. This paper mainly constructs a new algorithm (LBRM Algorithm) for rule extraction based on rough membership. The confidence principle is established based on rough membership. Thus, LBRM Algorithm is proposed by utilizing discretization and clearness strategies under the fuzzy environment, and is applied to both interval rules and general rules in fuzzy classification. LBRM Algorithm effectiveness is illustrated by a medical example. In particular, LBRM Algorithm integrates the confidence on both previous LBR Algorithm and fundamental rough membership, and has some improvements on rule confidence.

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A new customized document categorization scheme using rough membership

Cited by 17 publications

References 19 publications

Business Intelligence Through Personalised Location-Aware Service Delivery

Business Intelligence Through Personalised Location-Aware Service Delivery

Stereoscopic story visualization in literary works demonstrated by Shakespeare’s plays

LBRM Algorithm for Rule Extraction Based on Rough Membership

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