近年來亞洲地區國家產業群聚蓬勃發展,如中國大陸、東南亞、臺灣等…,對亞洲整體經濟發展有著重大影響。因此相對帶動產業群聚地區居住需求，然而，即使在產業聚落工作，卻也不見得能夠負擔高漲的房價，進而以租賃取代購買滿足居住的需求，過往學者著重於不動產買賣價值分析的研究較多，較少針對環境對於租賃價格的定義進行資料分析並且加以研究規則，因此，本研究依據大數據資料探勘的基礎，發展「科技產業聚落不動產租賃定價規則」的規則導向，期望能藉由規則的定義，提供科技產業聚落不動產租賃市場中的消費需求者更精準之定價規則供其參考。同時利用大數據資料及約略集合理論（Rough Set Theory）推導科技產業聚落周邊不動產租賃定價規則，使房仲業者定價能夠更趨於精準。本研究之主要內容旨在探索科技產業聚落不動產租賃市場之價格定義規則。有鑑於此，本研究將以消費者租賃需求期望偏好及基於約略集合之關聯規則探勘法，發展出符合科技產業聚落租賃房屋市場產品之顧客消費期望偏好之規則結果。本研究將依據台北市某大房屋租賃公司所擁有之歷史交易資料庫中，探討內湖科學園區及南港科學園區周邊物件之區位因素與租賃價格，實證本研究所推導之定價規則，可作為房屋租賃業者定價之依據。

There has been a boom in various aspects of industries in Mainland China, South East Asia and Taiwan recently. It has drastically influenced the entire economic development of Asia. Therefore, the need for residence of Technology clustering is gradually ascending; however, the expensive housing price is not affordable though working in the industrial clusters. That is to say, purchasing will be replaced by renting to meet the residential requirement. The study of real estate emphasized on analysis of price more but less on the analysis of rental pricing toward environment. Thus, the study is to develop the Decision Support System concerning “the Rental Rules of the real estate in Technology Clustering” on the basis of data-probing. By the foundation and schematization of this system and rules, we can supply references for consumers from the rental market of the real estate in technology clustering with greater accuracy in decision-making and assessing or evaluating; meanwhile, we use the big data to infer the rental rules of the real estate in technology clustering which makes the price of realty industry more accurate. The leading content of this study is to construct the Decision Support rules of the tenant consuming preference regarding the rental market of the real estate in technology clustering. Whereas, the study will construct the decision support system of the clients consuming preference in accordance with the rental market of the real estate in technology clustering on the basis of clients' preferences and the Association Rule of Rough Set Theory. The study will probe the transaction record database of one major house-renting company in Taipei that focuses on the location factors and lease prices of neighboring objects in Neihu Science Park, to prove feasibility of this study and the price-making rules, which can be the very dependence of price-making for realty industry.

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