Precise Marketing Data Mining Method of E-Commerce Platform Based on Association Rules

Zhang, Hong-ni; Dwivedi, Ashutosh Dhar

doi:10.1007/s11036-021-01886-3

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…The authors also optimized the association rules for achieving accurate data mining. Experimental results of the proposed solution showed that the integrity of the data mining was steadily at 80%, while the relative and average error were lower than 0.3% [ 93 ].…”

Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

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The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

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Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Step (1) generates G(A 1 ) and G(A 2 ) , step (2) generates ∪[G(A 1 )] and ∪[G(A 2 )], step (4) generates association rules, and step (5) generates redundant association rules.…”

Section: Generalized Association Rules Based On the Upper Approximationmentioning

confidence: 99%

“…(6) Return to (5). end while Output Generalized association rules and corresponding redundant association rules based on the upper approximation with (supp(ψ * ), con f (ψ * )).…”

Section: Algorithm 4 Mining Generalized Association Rules and Redunda...mentioning

confidence: 99%

“…In knowledge discovery in database (KDD), association rules mining (ARM) from transaction databases proposed in [1,2] have received considerable attention and wide applications, such as medical diagnosis [3,4], marketing planning [5,6], etc. ARM can be formally explained as follows: Let A = (U, A) be a transaction database, where U a non-empty finite set of transactions, A a non-empty finite set of items, each transaction u i ∈ U decides a mapping u i : A −→ {0, 1}, i.e., for any item a j ∈ A, the transaction u i has (or has not) the item a j ∈ A if u i (a j ) = 1 (or u i (a j ) = 0), each subset of A is called as an itemset.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Associate Rules Mining Based on Topology for Items of Transactional Data

Pei

Zhang³

et al. 2023

Mathematics

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A challenge in association rules’ mining is effectively reducing the time and space complexity in association rules mining with predefined minimum support and confidence thresholds from huge transaction databases. In this paper, we propose an efficient method based on the topology space of the itemset for mining associate rules from transaction databases. To do so, we deduce a binary relation on itemset, and construct a topology space of itemset based on the binary relation and the quotient lattice of the topology according to transactions of itemsets. Furthermore, we prove that all closed itemsets are included in the quotient lattice of the topology, and generators or minimal generators of every closed itemset can be easily obtained from an element of the quotient lattice. Formally, the topology on itemset represents more general associative relationship among items of transaction databases, the quotient lattice of the topology displays the hierarchical structures on all itemsets, and provide us a method to approximate any template of the itemset. Accordingly, we provide efficient algorithms to generate Min-Max association rules or reduce generalized association rules based on the lower approximation and the upper approximation of a template, respectively. The experiment results demonstrate that the proposed method is an alternative and efficient method to generate or reduce association rules from transaction databases.

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“…The proficient utilization of data mining technology facilitates ecommerce platforms in discerning alterations in consumer consumption patterns, variations in the level changes of consumer groups, and consumption levels. This enables the optimization of online marketing strategies and attaining greater benefits for enterprises [2].…”

Section: Introductionmentioning

confidence: 99%

Improving Marketing Strategies using Data Mining and Decision Support Systems in E-commerce Platforms

Huang

2023

Preprint

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Growing businesses need e-commerce systems; marketers must help analyze data and make decisions. Data mining and decision support systems optimize marketing campaigns by analyzing customer preferences and purchase habits. With this combination, personalization, trend spotting, client segmentation, and cross-selling improve customer satisfaction, conversion rates, and Return on Investment (ROI). This paper aims to conduct a thorough study on the optimization of e-commerce marketing strategy based on data mining and decision support systems to solve the issues of high-cost investment in e-commerce platforms, low efficiency of online marketing, and improve the sales performance of e-commerce platforms. First, a Browser / Server mode (B/S) architecture based on e-commerce marketing system is built. The system is broken down into interface, functional, and support layers. The support layer includes multiple system interfaces to achieve data exchange across various functions inside the network marketing channel. The functional layer includes network marketing, service monitoring and management, basic information management, and other modules. Subsequently, the e-commerce platform utilizes the decision tree algorithm to optimize its online marketing strategy. This involves filtering the data available on the platform and subsequently computing the data structure and dissimilarity matrix. The ultimate objective is to determine the closest data class distance. The decision tree data mining algorithm is utilized to optimize the online marketing strategy of the e-commerce platform. The method proposed in this paper has been validated through experimental verification and has demonstrated a robust data throughput capacity. It has also been shown to effectively enhance the efficiency of online marketing. Furthermore, the system designed in this paper has the potential to enhance the economic benefits of e-commerce platforms.

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Precise Marketing Data Mining Method of E-Commerce Platform Based on Association Rules

Cited by 8 publications

References 14 publications

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Efficient Associate Rules Mining Based on Topology for Items of Transactional Data

Improving Marketing Strategies using Data Mining and Decision Support Systems in E-commerce Platforms

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