| Summary: | 碩士 === 大同大學 === 資訊工程學系(所) === 92 === After the previous E-Commerce bubble has been broken a new era of e-commerce has just begun. Nowadays the majority of the public have high degree of acceptance in doing business over the Internet, each every company also provides E-Commerce platform, but where is the potential client in the market? It is worth to give us a deep thought.
In this study, Data Mining approach will be used to discuss which group of client is more initiative or which one tends to be more passive, and to find out their identical characteristics and discrepancy. Some of the E-Commerce soft wares that are currently used by many users are my test samples. Thus, if the similarities characteristics of these soft wares have been found, data Mining approach is very helpful to find out the most potential e-commerce software user in the market.
Data Mining has many different algorithms. Algorithm Apriori in Association rule is the method that we use in this study.
The main purpose of Apriori is to find out the high frequency group, its definition: if one group meets the minimum level of support, and it contains k-value we call it as k-group, and if it satisfies the minimum level of support we call it as the high frequency k-group; after we found the high-frequency group, we use the minimum level of confidence to judge whether the rule of relativity is established or not.
The first part takes the reason why the existing customers want to join e-commerce into consideration: the age of opening the store and whether they have former experience of selling product over the Internet, etc. and we input the information to obtain the result and presented it in percentage to show the reasons why the customer choose to use e-commerce and what are their demands. The result might show that having a great confidence on doing business online is because they have been successfully done it before, or it is because their demands for the company as they choose an information provider to be the working partner or etc.
In the second part all data processing procedure interfaces will be shown vividly to have a better visual effect.
In the third part, all data will be analyzed and current marketing data will be presented to the sales representative for them to practice in the real market and keep the record of the result. The margin error will probably around 40% to 50 % due to the unexpected individual behavior can not be taken into account during the calculation. Further explanation and solution on how to correct and suggest a plan is a big discovery and that help enterprise find many potential e-business customers.
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