在人力資源領域當中資料探勘的分類技術尚未受到廣泛的注目與應用。因此，豐富的HR資料也缺乏更為深入的應用。在知識管理流程當中，資料探勘技術提供了一個在大量資料內粹取與挖掘具有價值與意義的知識。而這樣的一項技術將會是人力資源專家在面對困難且未知的人才篩選的一項有利工具，過去人才篩選依賴多項因素，像是經驗、知識、績效與判斷能力等，事實上這樣的篩選條件已經不足夠．因為在這個知識經濟與VUCA的商業環境之下，今天促成某個人坐上某個位置的因素有可能隔天就不適用，但在人才管理當中，產出的定義是確保正確的人在正確的工作崗位上。綜合上述因素，在人才管理領域要如何篩選人才並且預測人才未來的可能發展成了每一個組織的挑戰與問題。而本研究將應用資料探勘當中決策樹的技術進行資料分析，透過探勘的方式尋找出影響在職時間的關鍵因素。本研究透過K公司所提供的資料，將其進行決策樹分析，分析出影響員工在職時間的關鍵因素。

The use of data mining technology in human resources management has yet not received widely attention. There is a lack of in-depth application of extensive HR data. In respect to the process of knowledge management (KM), data mining provides valuable and meaningful knowledge by digging out pieces of information in the batches of data. The technology will be a powerful tool for HR professionals in face of the difficult talent selection. In the past, the screening job depends on a number of factors: experience, knowledge, performance, ability to judge, and etc. In fact, the criteria are not enough in such knowledge-based economy and VUCA (Volatility, Uncertainty, Complexity, Ambiguity) business environment.
Nowadays, the factors contributing to the speculation of one position may not apply to work the next morning. However, in talent management, the definition of output is to ensure that the right person is at the right place to work. Therefore, talent selection and the prediction of talent development have been future challenges and problems of each organization. The database of this study was provided by company K. The Data mining techniques of decision tree was applied to find out the key factors affecting employee tenure.

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參考書目
1. 簡禎富、許嘉裕(2014)。資料挖礦與大數據分析 Data Mining & Big Data Analytics。出版商：前程文化事業有限公司。

參考網站
1. http://www-01.ibm.com/support/knowledgecenter/