Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm

Silva, Jesus; Varela, Noel; López, Luz Adriana Borrero; Millan, Rafael Humberto Rojas

doi:10.1016/j.procs.2019.04.173

Cited by 31 publications

(11 citation statements)

References 14 publications

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“…The mining process is based on the horizontal format and completes the extraction of rules through iterative search strategy. After data connection and pruning, the itemsets satisfying the association rules are formed [8][9][10][11]. If one itemset satisfies the minimum support and a certain confidence, it is defined as a frequent itemset.…”

Section: Frequent Itemset Mining Based On Apriorimentioning

confidence: 99%

Improved Probabilistic Frequent Itemset Analysis Strategy of Learning Behaviors Based on Eclat Framework

Xia¹

2022

Advances in Decision Making

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Interactive learning environment is the key support for education decision making, the corresponding analytics and methodology are the important part of educational technology research and development. As an important part and the research challenge, learning behaviors are uncertain and produce complex data relationships, which makes the learning analysis process more difficult. This chapter studies the feasibility of Eclat framework applying in educational decision making and get the corresponding the data analysis results. We take probabilistic frequent itemsets and association rules as research objectives, extract and standardize multiple data subsets; Based on Eclat framework, using data vertical format, we design and improve the models and algorithms in the process of data management and processing. The results show that the improved models and algorithms are effective and feasible. On the premise of ensuring robustness and stability, the mining quality of probabilistic frequent itemsets and association rules is guaranteed, which is conducive to the construction of key execution topology of learning behaviors, and improves the accuracy and reliability of data association analysis and decision prediction. The whole analysis methods and demonstration processes can provide references for the study of interactive learning environment, as well as decision suggestions and predictive feedback.

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Section: Frequent Itemset Mining Based On Apriorimentioning

confidence: 99%

Improved Probabilistic Frequent Itemset Analysis Strategy of Learning Behaviors Based on Eclat Framework

Xia¹

2022

Advances in Decision Making

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“…While some researcher employs grounded theory and proposed domain-specific variables in customer profiling [4] [10-13], most researchers favored the use of the RFM model. The popular usage of RFM or its various models covers many domains such as banking [14], Hotel industry [15], Small and Medium Enterprises (SME) [16], grocery retail industry [3], and online retail industry [5] [11] [17].…”

Section: A Theoretical Model In Customer Profilingmentioning

confidence: 99%

Customer Profiling for Malaysia Online Retail Industry using K-Means Clustering and RM Model

Kit¹,

Firdaus²

2021

IJACSA

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Malaysia's online retail industry is growing sophisticated for the past years and is not expected to stop growing in the following years. Meanwhile, customers are becoming smarter about buying. Online Retailers have to identify and understand their customer needs to provide appropriate services/products to the demanding customer and attracting new customers. Customer profiling is a method that helps retailers to understand their customers. This study examines the usefulness of the LRFMP model (Length, Recency, Frequency, Monetary, and Periodicity), the models that comprised part of its variables, and its predecessor RFM model using the Silhouette Index test. Furthermore, an automated Elbow Method was employed and its usefulness was compared against the conventional visual analytics. As result, the RM model was selected as the finest model in performing K-Means Clustering in the given context. Despite the unusefulness of the LRFMP model in K-Means Clustering, some of its variables remained useful in the customer profiling process by providing extra information on cluster characteristics. Moreover, the effect of sample size on cluster validity was investigated. Lastly, the limitations and future research recommendations are discussed alongside the discussion to bridge for future works.

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“…J. Silva et al [13] proposed the method of classifying customers in detail through the extraction of association rules. It utilizes an association rule algorithm in order to find associations between various types of customers and products and establish a marketing strategy.…”

Section: Extracting Knowledge Using Associative Rulesmentioning

confidence: 99%

Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score

2020

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This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are generated. News data are first collected through crawling and then are converted into a corpus through preprocessing. Unnecessary data are removed through preprocessing including lowercase conversion, removal of punctuation marks and stop words. In the document term matrix, words are extracted and then transactions are generated. In the data cleaning process, the Apriori algorithm is applied to generate association rules and make a knowledge graph. To optimize the generated knowledge graph, the proposed method utilizes TF-IDF based ranking scores to remove terms with low scores and recreate transactions. Based on the result, the association rule algorithm is applied to create an optimized knowledge model. The performance is evaluated in rule generation speed and usefulness of association rules. The association rule generation speed of the proposed method is about 22 seconds faster. And the lift value of the proposed method for usefulness is about 0.43 to 2.51 higher than that of each one of conventional association rule algorithms.

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Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm

Cited by 31 publications

References 14 publications

Improved Probabilistic Frequent Itemset Analysis Strategy of Learning Behaviors Based on Eclat Framework

Improved Probabilistic Frequent Itemset Analysis Strategy of Learning Behaviors Based on Eclat Framework

Customer Profiling for Malaysia Online Retail Industry using K-Means Clustering and RM Model

Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score

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