| Summary: | 碩士 === 雲林科技大學 === 資訊管理系碩士班 === 96 === The advent of modern information technology results in prosperous development of electronic commerce in the Internet so that Internet shopping is becoming more and more popular. However, many shoppers still face a lot of inconveniences nowadays when they conduct on-line shopping. The problems include the following：(1) There are too many products in an e-commerce shop store. (2) Similar products which have about the same functionally may have very different prices due to different brands.In the situation, it is essential for consumers to spend time comparing and choosing the product in order to acquire a product which matches his need with a reasonable price. However, manually comparing product information revealed on a website is tedious and costly. We plan to develop a framework of analyzing on-line products to support consumers effectively choosing appropriate commodity from the Internet. With the product of digital camera as an example, we collect product data including function of digital camera, product information, price and so on. They will be clustered according to clustering analysis by ViSOM from different aspects such as consumers, price, competitive strategy of manufacturers. The result of the experiment can help to choose a digital camera which fits the consumer’s need. In addition, we can provide effective strategy to manufactures of digital camera from different analysis aspects. In our experiments, we use the real digital camera datasets collected in the fourth season of 2007 from the Web to analyze. The preliminarily experimental results demonstrate that the proposed framework of data mining is feasible for online shopping in terms of seeking out a good buy for consumers and comparing product and pricing strategies for manufacturers. In the future, we hope to extend the analytic model further to find out more useful knowledge from the collected product dataset.
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