Itemset trees for targeted association querying

Kubát, Miroslav; Hafez, Alaaeldin M.; Raghavan, Vijay V.; Lekkala, J.; Chen, Wei Kian

doi:10.1109/tkde.2003.1245290

Cited by 73 publications

(63 citation statements)

References 25 publications

(33 reference statements)

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“…Aggarval et al . (2002) and Kubat et al . (2003) identified association rules from market basket analysis.…”

Section: Researches On Crm Using Data Mining Techniquesmentioning

confidence: 96%

VIP-targeted CRM strategies in an open market

Lee¹,

Shim²,

Suh³

2015

Journal of the Korean Data and Information Science Society

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Nowadays, an open-market which provides sellers and consumers a cyber place for making a transaction over the Internet has emerged as a prevalent sales channel because of convenience and relatively low price it provides. However, there are few studies about CRM strategies based on VIP consumers for an open-market even though understanding VIP consumers' behaviors in open-markets is important to increase its revenue. Therefore, we propose CRM strategies targeted on VIP customers, obtained by analyzing the transaction data of VIP customers from an open-market using data mining techniques. To that end, we first defined the VIP customers in terms of recency, frequency and monetary (RFM) values. Then, we used data mining techniques to develop a model which best classifies and identifies influential factors customers into VIPs or non-VIPs. We also validate each of promotion types in the aspect of effectiveness and identify association rules among the types. Then, based on the findings from these experiments, we propose strategies from the perspectives of CRM dimensions for the open-market to thrive.

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“…Aggarval et al . (2002) and Kubat et al . (2003) identified association rules from market basket analysis.…”

Section: Researches On Crm Using Data Mining Techniquesmentioning

confidence: 96%

VIP-targeted CRM strategies in an open market

Lee¹,

Shim²,

Suh³

2015

Journal of the Korean Data and Information Science Society

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“…Raghavan, J.R. Lekkala, W.K. Chen introduce [2] concept of item-set Trees for Targeted Association Querying. In this paper we can have faster detection of item-sets and association rules.…”

Section: Related Workmentioning

confidence: 99%

“…There are several capable algorithms to insert transactions into the item-set tree and to count frequencies of item-sets for queries about power of association among items. [2] One of the efficient tree structures is memory efficient itemset tree. An efficient data structure for performing targeted queries for item-set mining and association rule mining is the MEIT.…”

Section: Introductionmentioning

confidence: 99%

A Recent Review on Itemset Tree Mining: MEIT Technique

Patel¹,

Patel²

2015

IJCA

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Association rule mining forms the core of data mining and it is termed as one of the well-researched techniques of data mining. It aims to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Hence, Association rule mining is imperative to mine patterns and then generate rules from these obtained patterns. This paper provides the preliminaries of basic concepts about Itemset mining and survey the list of existing tree structure algorithms. These algorithms include various tasks such as fast query processing, optimizing memory space and reducing tree construction time. For mining maximal frequent pattern various algorithms used which optimization the search space for pruning.

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“…Adomavicius and Tuzhilin (2001) examined association rules for one to one marketing. Aggarval and Yu (2002) and Kubat et al (2003) identified association rules from market basket analysis. Changchien et al (2004) …”

Section: Data Mining For Customer Relationship Managementmentioning

confidence: 99%