臺灣博碩士論文加值系統

隨著資訊科技日新月異，帶動網際網路的快速發展，因此，造就網路購物的消費型態。網路購物的產品資料繁多，將導致消費者於搜尋商品資料會遭遇(1) 同質性的商品資料太多，(2)購物網站所提供的資料繁雜。消費者須花費時間選購商品資訊又須要進行商品的比價作業，即花費時間冗長，費時又費力。
本研究提出一套分析模型，針對從網際網路上收集的資料進行商品資訊的分析，藉此分析結果以協助消費者更有效率選擇最適當的產品。以數位相機為例，我們收集網路上各廠牌的數位相機與產品資訊，針對價格、相機功能、廠商競爭策略等觀點，以視覺化自組映射圖進行分群分析，藉由分析各群所得之結果能協助消費者選擇符合自己所需求的數位相機，以解決在網路購物中商品資訊太多之問題，而造成消費者不易選購產品的缺失，也提供了更多比較性的資訊作為消費者選購產品依據；另外，藉由各個不同的觀點來分析，可提供給數位相機產業做為策略參考之用。
從我們的實驗結果得以證實我們所提出來的分析模型是可行的，將真實資料集經由視覺化自組映射圖分群後，再透過不同觀點來進行分析，得以讓我們快速的提供消費者選購的參考及清楚的比較出各大廠商間的產品及價格策略，未來我們更期望能利用這樣的分析模型找出更多更有用的知識並在其它領域的資訊中也能應用此模型快速的找出不同觀點的整合性知識。

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.

摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
一、 緒論 1
1.1. 動機 1
1.2. 目的 1
1.3. 研究限制 2
1.4. 論文架構 2
二、 文獻探討 3
2.1. 資訊擷取 3
2.2. 自組映射圖 4
2.3. 視覺化自組映射圖 6
2.4. 分群績效指標 6
三、 研究架構 7
3.1 相機資料擷取流程 8
3.2 視覺化自組映射圖分群 9
3.3 群組資料特徵分析 12
3.4 優勢分析 14
四、 實驗 15
4.1 人工資料集 15
4.2 相機真實資料集 19
4.2.1六群組分析 20
4.2.1.1價格分佈 21
4.2.1.2群內特徵 22
4.2.1.3群間特徵 22
4.2.2九群組分析 24
4.2.2.1價格分佈 24
4.2.2.2群內特徵 25
4.2.2.3群間特徵 26
4.2.3十三群組分析 27
4.2.3.1價格分佈 28
4.2.3.2群內特徵 30
4.2.3.3群間特徵 31
4.3各品牌群內與群間分析 33
4.4小結 34
五、結論與未來工作 34
5.1結論 34
5.2未來工作 35
參考文獻 35
附錄一 數位相機功能屬性一覽表 37

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