Fast clustering algorithm of commodity association big data sparse network
| Autor: | Xiaohuan Yang, Hailan Pan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Apriori algorithm
Association rule learning Computer science business.industry Strategy and Management Commodity Big data 020206 networking & telecommunications 02 engineering and technology computer.software_genre 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Safety Risk Reliability and Quality Cluster analysis Literature survey business Transaction data Database transaction computer |
| Zdroj: | International Journal of System Assurance Engineering and Management. 12:667-674 |
| ISSN: | 0976-4348 0975-6809 |
| DOI: | 10.1007/s13198-021-01060-8 |
| Popis: | How to dig out the business perspectives and market rules behind commodity transaction data, explore the relationship between commodities, so as to more scientifically and rationally classify and promote commodity categories and improve commodity sales performance for e-commerce companies has become a recent research hotspot. To this end, this paper proposes to use clustering algorithm to explore the hidden laws of commodity-related big data. This article first consults a large amount of information through the literature survey method, systematically summarizes the relevant theoretical knowledge of the association rule method and clustering algorithm and gives a detailed introduction to its application in the commodity association big data mining. The research in this area has laid a sufficient theoretical foundation; after that, the Apriori algorithm in the association rules and the K-means algorithm in the clustering algorithm were used to carry out the fast clustering algorithm experiment of the commodity-related big data sparse network and the commodity transaction data was introduced in detail. The process of association analysis and cluster analysis; then taking China’s well-known e-commerce platform Jingdong Mall as an example, by investigating the commodity transaction records of Jingdong Mall in the 4th week of July, the association and cluster analysis of its commodity transaction data were found. Among them, mobile phones and Bluetooth earphone, laptops and Bluetooth earphone, laptops and hard disks have the highest correlation and their confidence thresholds have reached 25%, 35 and 40% respectively. Finally, when the clustering results were tested, they were also found in the store. Strengthening the push and shopping guide of highly relevant product combinations on the website pages will increase the sales of products. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink