隨著科技的持續進步，行銷策略也在不斷演變。過往企業的行銷方式如果沒有與時俱進，將難以推動客戶購買更多樣化的產品。本篇研究旨在制定可行的行銷策略，利用化工業的銷售數據挖掘潛在的產品組合，並主動向客戶推廣，藉此增加產品銷售的種類，從而打破舊有行銷模式所造成的行銷範圍侷限。
而在本篇研究之中，依照產品類別選取三大類作為研究的對象，並將各大類中對應於單一客戶的出貨資料聚合形成一筆交易，再進一步將三大類的交易資料，逐一套入 FP-growth 演算法，以發掘在四個層級（大陸、地區、國家、產品）之中，前十筆具有最高提升度的關聯規則。透過本篇依照產品類別探勘關聯規則，不僅能決定產品組合內部的產品種類及種類數，促使產品組合精準地符合各地客戶的需求，更能基於多種關聯規則獲取商業洞見，從中找到客戶下單產品的原因，以此作為制定行銷策略的參考依據。

With the ongoing advancement of technology, marketing strategies are continually evolving. Traditional marketing methods that don’t align with contemporary developments may encounter significant challenges in motivating customers to diversify their product purchases. This study aims to develop practical marketing strategies by discovering association rules from the sales data of a chemical industry. The rules can reveal potential product combinations for promoting them to customers proactively, and new marketing models can be developed to enhance the diversity of products sold.
In this study, the chemical products are divided into three categories, and the orders corresponding to a customer for each category are aggregated to form a transaction. The FP-growth algorithm is then applied on the transactions for each category to discover the top ten association rules with the largest lifts for four levels: continents, regions, countries, and products. The rules for each category in each level provide meaningful business insights to clarify the underlying factors behind customer purchasing decisions and serve as valuable references for developing marketing strategies.

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