A Novel J2ME Service for Mining Incremental Patterns in Mobile Computing

Dubey, Ashutosh Kumar; Shandilya, Shishir Kumar

doi:10.1007/978-3-642-15766-0_23

Cited by 20 publications

(10 citation statements)

References 16 publications

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“…K-means clustering, fuzzy C-means and optimization techniques are used in several research works like [6][7][8][9][10]. This also includes the use of association rule mining as the meaningful data should be extracted [11][12][13][14][15].…”

Section: *Author For Correspondencementioning

confidence: 99%

A survey and analysis of page ranking through data mining and advanced techniques

Singh¹,

Patidar²,

Sahu³

2018

IJATEE

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Section: *Author For Correspondencementioning

confidence: 99%

A survey and analysis of page ranking through data mining and advanced techniques

Singh¹,

Patidar²,

Sahu³

2018

IJATEE

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“…It can be smarter to locate a legitimate bunched approach to arrange the records, at that point apply some characterization criteria which will be fulfilled some edge an incentive to give the compelled method for these issue. It can be accomplished through affiliation lead mining [7], we can utilize apportioning system additionally in light of the fact that it can diminish the looking time and upgrade the seeking ability [8,9]. For characterization we can utilize affiliation run mining with some grouping procedures like k-means and fuzzy c-means, it will be a superior choice [10].…”

Section: *Author For Correspondencementioning

confidence: 99%

A survey and analysis based on topic based classification

Sikarwar¹,

Patidar²,

Kushwah³

2018

IJATEE

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In this paper a survey and analysis based on topic based data classification has been presented. It includes the topic based data orientation, data categorization, document clustering, etc. This study provides the analytical way to analyze the methods previously published and provide explorative way of the approaches presented. It also provides the discussion based on the attributes and property used and explored. This study provides the discussion of different partitioning algorithm, different grouping methods and classification approaches.Based on the study, future enhancements have been suggested.

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“…In previous studies, various techniques and methodologies to track, record and analyze customer behavior patterns have been proposed for various applications. These include cross marketing in business domains [88,89], mobile environment planning [32,90] and website design and management [91,92].…”

Section: Customers' Purchasing Behaviorsmentioning

confidence: 99%

Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making

2017

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Due to increasing concerns about environmental protection, the environmental sustainability of businesses has been widely considered in the manufacturing and supply chain context. Further, its adoption has been implemented in the retail industry for marketing field, including green product promotion. This study aimed to propose a customer purchasing behavior analysis as an alternative for supporting decision-making in order to promote green products in retail stores. Hence, right-on-target marketing strategies can be implemented appropriately. The study was carried out using shopping path data collected by radio frequency identification (RFID) from a large retail store in Seoul, South Korea. In addition, the store layout and its traffic were also analyzed. This method is expected to help experts providing appropriate decision alternatives. In addition, it can help retailers in order to increase product sales and achieve high levels of customer satisfaction.

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A Novel J2ME Service for Mining Incremental Patterns in Mobile Computing

Cited by 20 publications

References 16 publications

A survey and analysis of page ranking through data mining and advanced techniques

A survey and analysis of page ranking through data mining and advanced techniques

A survey and analysis based on topic based classification

Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making

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