scholarly journals Effectively Optimizing the Patterns with Dynamic Behaviors of the Transactions by using Data Mining Tool

2019 ◽  
Vol 8 (12) ◽  
pp. 2312-2316
Keyword(s):  
Data Mining ◽  
Reaction Time ◽  
Market Basket ◽  
Data Mining Tool ◽  
Testing Issue ◽  
Mining Tool ◽  
Test Investigation ◽  
Using Data ◽  
The Right ◽  
Time Information

Basic management and understanding the conducted of the client has turned out to be indispensable and testing issue for associations to continue their situation in the focused markets. Mechanical advancements have cleared leap forward in quicker handling of questions and sub-second reaction time. Information mining devices have turned out to be surest weapon for breaking down colossal measure of information and leap forward in settling on right choices. The target of this paper is to break down the colossal measure of information subsequently abusing the buyer conduct and settle on the right choice prompting aggressive edge over adversaries. Test investigation has been done utilizing affiliation principles utilizing Market Basket examination toward demonstrate its value more the regular systems.

The Research on C2C E-Commerce Website Using Data Mining Tools to Improve the Network Marketing Effect

2013 ◽  
Vol 433-435 ◽  
pp. 1885-1889
Author(s):  
Lu Feng ◽  
Zhan Quan Wen ◽  
Jie Mei Lin
Keyword(s):  
Data Mining ◽  
Analysis Method ◽  
Network Marketing ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Using Data ◽  
Object Of Study ◽  
Hyperlink Analysis ◽  
Mining Tools

We used the principle of hyperlink analysis method to mine the website data according to the indicators of the hyperlink analysis. We selected Taobao.com as an object of study. The evaluation indicators of network marketing effect were page views, sales quantity, sales, the number of adding store to bookmark . According to our research, we find Taobao.com stores can use data mining tool to obtain the very good marketing effect.

scholarly journals Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations

2019 ◽  
Vol 2 (2) ◽  
pp. 107
Author(s):  
Imaduddin Syukra ◽  
Assad Hidayat ◽  
Muhammad Zakiy Fauzi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Market Basket ◽  
Transaction Data ◽  
A Value ◽  
Using Data ◽  
The Right ◽  
A Company ◽  
Technical Recommendations

212 Mart Rambutan Street on Pekanbaru City is a company engaged in retail. Meeting the needs of consumers and making the right decision in determining the sales strategy is a must. One way to find out market conditions is to observe sales transaction data using data mining. The data mining method commonly used to analyze market basket (Market Basket Analysis) is the Association Rule. The Association Rule can provide product recommendations and promotions, so that the marketing strategy is more targeted and the items promoted are the customer's needs. At 212 Mart, the determination of product promotion is obtained from the analysis of sales transaction data reports, which are based on the most sold products and the expiration date. Often the product being promoted does not fit the customer's needs. The purpose of this study is to apply the K-Medoids algorithm for clustering on FP-Growth in producing product recommendation rules on a large number of datasets so that they can provide technical recommendations / new ways to the 212 Mart in determining product promotions. The results obtained are from the experiments the number of clusters 3 to 9 obtained optimal clusters of 3 clusters based on the validity test of the Davies Bouldin Index with a value of 0.678. With a minimum support value of 5% - 9% and a minimum value of 50% confidence, the result is that the Association Rule is found only in cluster 3 with 5 rules.

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