Clustering of bank customers based on lifetime value using data mining methods

Hasheminejad, Seyed Mohammad Hossein; Khorrami, Mojgan

doi:10.3233/idt-190176

Cited by 8 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data mining techniques generally consist of three stages: data preparation, pattern finding, and result interpretation and evaluation. These three stages can be divided into seven steps, as shown in Figure 1 [11][12][13].…”

Section: Msda Analysis Based On A-amentioning

confidence: 99%

See 1 more Smart Citation

A Priori Algorithm Based Network Security Situational Awareness Multi-Source Data Correlation Analysis Method

Li,

Zhang

et al. 2023

JCSANDM

View full text Add to dashboard Cite

In the context of the information age, the Internet has developed rapidly, but the accompanying network security threats have also become an issue that cannot be ignored. In order to effectively respond to these threats and improve the data processing capabilities of network security situational awareness, the study focuses on the challenges of multi-source data processing and proposes a multi-source data association analysis method based on the A priori algorithm. This method aims to deeply explore the implicit relationships between data and provide stronger support for network attack detection. In addition, the study also designed a multi-level evaluation method based on coefficient of variation indicators, aiming to provide a more objective and comprehensive evaluation of the detection results. After a series of experimental verification, the proposed correlation analysis method has achieved significant results in detecting phishing attacks and DOS attacks, with detection rates of 90.3% and 93.8%, respectively. At the same time, the multi-level evaluation method has also been experimentally proven to provide more reasonable and accurate results for data evaluation. The methods and technologies proposed in the study can not only improve the multi-source data processing ability of network security situational awareness, but also provide valuable references for future network security research and practice.

show abstract

Section: Msda Analysis Based On A-amentioning

confidence: 99%

“…In Equation (12), Zj indicates the comprehensive data value, and Z ij indicates the jth indicator of the ith data of a certain evaluation data. Then build the decision matrix of the evaluation object, and finally use Equation (13) to calculate the indicator weights can be.…”

Section: A Multi-level Approach To Assessing Cyber Security Posturementioning

confidence: 99%

A Priori Algorithm Based Network Security Situational Awareness Multi-Source Data Correlation Analysis Method

Li,

Zhang

et al. 2023

JCSANDM

View full text Add to dashboard Cite

show abstract

“…e University of California, Berkeley, conducted an experiment to generate documents using the gpt-3i5j pretraining model. In the experiment, Researchers input the title and introduction of the experimental paper and generate the model and other paper contents [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Practical Skills of Business English Correspondence Writing Based on Data Mining Algorithm

Liu

Habil

2022

Scientific Programming

View full text Add to dashboard Cite

English correspondence writing has become a necessary skill for every scientific researcher and high-tech talents. An English correspondence writing auxiliary writing system can help nonnative English speakers make up for the lack of professional expression. The key factor of business English correspondence writing system is the construction of knowledge base. To improve the business English correspondence writing knowledge base, we need to mine frequent patterns of sentences in each category. The purpose of this topic is to improve and supplement the knowledge base for the business English correspondence writing system and propose frequent pattern mining for sentences in each category, so as to improve the writing knowledge base for the business English correspondence writing system. Firstly, we crawl a large number of business English letters and telegrams from the Internet, extract the relevant summary information, then store it, and preliminarily construct a corpus based on sentences. Then, we do some research on the structure of business English correspondence abstracts, mark the sentences in the corpus and count the relevant information, and have a certain understanding of their writing methods. Finally, we mine frequent patterns for sentences in each category, so as to improve the knowledge base of summary writing for the business English correspondence writing system. In the experiment, we use the classical FP growth algorithm as the mining method. The experiment shows that the frequent patterns between 3 and 6 words have been mined to a certain extent. By gradually improving the mining strategy, the quality of mining results has been improved and the writing effect of business English correspondence of scientific researchers has been improved.

show abstract

“…Hasheminejad & Khorrami [6] used two clustering algorithms, i.e., K-means and CPSOII, to segment customers. By analyzing the dataset, they found that, compared to K-means, the advantage of CPSOII was that it could determine the number of clusters automatically.…”

Section: Introductionmentioning

confidence: 99%

Research on Customer Relationship Segmentation of Apparel Retail Industry through Data Mining

Zhu

2023

HighTech. Innov. J.

View full text Add to dashboard Cite

Objectives: This paper aims to segment customers in the apparel retail industry using data mining techniques. Methods: First, a customer segmentation model was constructed, and then the K-means algorithm was used to classify customers based on indicators from the model. The classification effectiveness was enhanced by introducing indicator feature weights. A case study was also conducted. Findings: When the value of K was 4, the K-means algorithm achieved the best classification performance. The improved K-means algorithm outperformed the traditional K-means algorithm in terms of classification effectiveness. The improved K-means algorithm categorized customers into premium customers, important customers, regular customers, and churned customers. Different marketing suggestions were proposed to manufacturers. Novelty: The novelty of this article lies in the introduction of feature weights for indicators, which allows for a distinction between their importance and improves classification effectiveness. Doi: 10.28991/HIJ-2023-04-02-05 Full Text: PDF

show abstract

Clustering of bank customers based on lifetime value using data mining methods

Cited by 8 publications

References 31 publications

A Priori Algorithm Based Network Security Situational Awareness Multi-Source Data Correlation Analysis Method

A Priori Algorithm Based Network Security Situational Awareness Multi-Source Data Correlation Analysis Method

Practical Skills of Business English Correspondence Writing Based on Data Mining Algorithm

Research on Customer Relationship Segmentation of Apparel Retail Industry through Data Mining

Contact Info

Product

Resources

About