農作物交易價、量會因季節、氣候的變遷、消費行為、政策因素等多種因素而波動。本研究以分析全台農產品批發市場之交易價量資料作為研究標的。發展一套農作物的價量(二維)關係為基礎之分群方式。此方法透過資料正規化找出農產品之特徵值來代表每種作物，再以階層式分群法探討農作物彼此之間的相似性。結果發現農作物之價量關係具有數個特定的價量變動特徵，依此了解及分析農作物價量變動趨勢。本研究將558種作物依兩階段分群方法進行分群，第一階段為依據價量的外型特徵進行分群，以價量關係外型的相似程度作為該階段的分群結果；第二階段再依第一階段的分群結果，依交易價格和交易量進行分群，實驗結果發現兩階段分群方法與一階段分群方法相比，兩階段分群方法的SSE較小。希望藉由此模型，將農產品的價量資訊揭露，提供消費者購買時和經營者進貨時的參考依據。

The sale prices and transaction volumes of agricultural crops are fluctuated depending on multiple factors such as seasonal effect, climate change, consumer purchasing behavior, and so on. Analyzing the dynamic between sale price and transaction volume of agricultural goods can enhance the retailers’ capability of understanding the market trend. This research developed a two-step clustering method to conduct the data clustering on agriculture crop price-volume (P-V) data. The first step, for each crop, we proposed the p-v shape characteristics including the average, largest, and smallest price and volume values, the slope of p-v distribution, and R square of p-v linear regression to represent the shape of p-v distribution. The hierarchical clustering method was applied on the developed characteristic dataset to cluster 558 crops to several groups. The second step continued to perform the hierarchical clustering method on the price and volume dataset of each group to study the p-v seasonal pattern. The experimental results show the two-stage clustering method is able to have smaller sum of square error (SEE) while comparing with one-stage method. Except the better clustering performance, the proposed two-stage method also has the managerial advantage on providing easy-to-read clustering structure. The agriculture crop p-v clustering result can be provided to either customers or retailers for better information sharing and proactive marketing strategy.

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