Knowledge discovery of customer purchasing intentions by plausible-frequent itemsets from uncertain data

Weng, Cheng-Hsiung; Huang, Tony Cheng-Kui

doi:10.1007/s10489-015-0669-7

Cited by 7 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the introduction of ARM by Agrawal in [ 3 , 11 ], ARM has been widely utilized to extract useful and understandable patterns of data from a large amount of data. A challenge associated with ARM is the market basket problem, which has its origins in the study of consumer purchasing patterns in retail stores [ 12 ]. However, ARM and data mining have applications beyond this specific setting [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

A novel association rule mining approach using TID intermediate itemset

et al. 2018

View full text Add to dashboard Cite

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.

show abstract

Section: Introductionmentioning

confidence: 99%

A novel association rule mining approach using TID intermediate itemset

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Thus, inferring customer profiles from transaction data becomes an attractive alternative since transaction data provide a full view of the market (Lian, Xu, and Zhang 2019). Second, the idea of "a customer's transaction data can reflect purchasing behavior of that customer" is extended to the idea of "similar grocery stores can be grouped into the same cluster by detecting similar purchasing behavior of their customers" (Liao and Chang 2016;Weng and Huang 2015). Third, the purchasing behavior in this study are detected not just by an item or a few items as in (Chiang and Yang 2018) rather all of the items in the global transaction data are used (the main database of the retailer).…”

Section: Development Of a New Store Segmentation Approachmentioning

confidence: 99%

Retail analytics: store segmentation using Rule-Based Purchasing behavior analysis

Bilgiç

Çakır

Kantardzic

et al. 2021

The International Review of Retail, Distribution and Consumer R

View full text Add to dashboard Cite

Retailers are facing challenges in making sense of the significant amount of data for better understanding of their customers. While retail analytics plays an increasingly important role in successful retailing management, comprehensive store segmentation based on a Data Mining-based Retail Analytics is still an under-researched area. This study seeks to address this gap by developing a novel approach to segment the stores of retail chains based on "purchasing behavior of customers" and applying it in a case study. The applicability and benefits of using Data Mining techniques to examine purchasing behavior and identify store segments are demonstrated in a case study of a global retail chain in Istanbul, Turkey. Over 600K transaction data of a global grocery retailer are analyzed and 175 stores in İstanbul are successfully segmented into five segments. The results suggest that the proposed new retail analytics approach enables the retail chain to identify clusters of stores in different regions using all transaction data and advances our understanding of store segmentation at the store level. The proposed approach will provide the retail chain the opportunity to manage store clusters by making data-driven decisions in marketing, customer relationship management, supply chain management, inventory management and demand forecasting.

show abstract

“…Businesses have recently started using various technologies for management tasks, marketing planning and knowledge discovery (Dennis et al , 2001; Daim, 2014; Giatsoglou et al , 2016; Kenney and Gudergan, 2006; Monino, 2016; Smith, 2008; Weng and Huang, 2015). Their objective is to gain customer loyalty and discover contributions through customer value.…”

Section: Introductionmentioning

confidence: 99%

Knowledge acquisition of association rules from the customer-lifetime-value perspective

Weng

Huang

2018

Self Cite

View full text Add to dashboard Cite

Purpose Customer lifetime value (CLV) scoring is highly effective when applied to marketing databases. Some researchers have extended the traditional association rule problem by associating a weight with each item in a transaction. However, studies of association rule mining have considered the relative benefits or significance of “items” rather than “transactions” belonging to different customers. Because not all customers are financially attractive to firms, it is crucial that their profitability be determined and that transactions be weighted according to CLV. This study aims to discover association rules from the CLV perspective. Design/methodology/approach This study extended the traditional association rule problem by allowing the association of CLV weight with a transaction to reflect the interest and intensity of customer values. Furthermore, the authors proposed a new algorithm, frequent itemsets of CLV weight (FICLV), to discover frequent itemsets from CLV-weighted transactions. Findings Experimental results from the survey data indicate that the proposed FICLV algorithm can discover valuable frequent itemsets. Moreover, the frequent itemsets identified using the FICLV algorithm outperform those discovered through conventional approaches for predicting customer purchasing itemsets in the coming period. Originality/value This study is the first to introduce the optimum approach for discovering frequent itemsets from transactions through considering CLV.

show abstract

Knowledge discovery of customer purchasing intentions by plausible-frequent itemsets from uncertain data

Cited by 7 publications

References 37 publications

A novel association rule mining approach using TID intermediate itemset

A novel association rule mining approach using TID intermediate itemset

Retail analytics: store segmentation using Rule-Based Purchasing behavior analysis

Knowledge acquisition of association rules from the customer-lifetime-value perspective

Contact Info

Product

Resources

About