從交易資料庫中利用關聯規則的挖掘可以找出商品之間的關聯性，對於行銷推廣、商品搭配、商品貨架設計、生產排程等有絕大的幫助。傳統關聯規則挖掘方式只能針對單店的環境來挖掘出關聯規則，但在連鎖系列商店中，各家商店為了提高競爭力，每一家商店在不同的地點及時節會販賣不一樣的商品。例如醫院附近連鎖店所販賣的醫療性用品、觀光地區附近所販賣的觀光性商品、夏天所販賣的冰品、以及冬天所販賣的火鍋、特殊節日所販賣的禮品等。傳統關聯規則挖掘方式對於這些季節性及地區性商品在計算support值時都一視同仁地處理。此種方式將會造成其support值的低估而忽略了該商品於短期內或某區域內造成熱賣的事實。
為了解決傳統關聯規則挖掘方式應用在多商店環境時所產生的問題，我們提出了包含時間地點的關聯規則挖掘方式，此方法在計算不同商品的support值時，必須考量到不同的商品具有不同的上架地點及時間，而不是一視同仁地處理，如此算出來的support值才是正確的。而對於正確的confidence值的計算方式，我們也另外提出一個演算法來解決。
最後實驗模擬的結果證明，傳統關聯規則挖掘方式如果應用在多商店的環境之下時，將會造成釵h地區性或季節性商品的關聯規則無法被挖掘出來，而使用包含時間地點的關聯規則挖掘方式時，將可以解決這些問題。

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