數據挖掘(Data Mining,DM)又稱數據庫中的知識發現(Knowledge Discover in Database,KDD),是目前人工智能和數據庫領域研究的熱點問題,所謂數據挖掘是指從數據庫的大量數據中揭示出隱含的、先前未知的並有潛在價值的信息的非平凡過程。數據挖掘是一種決策支持過程,它主要基於人工智能、機器學習、模式識別、統計學、數據庫、可視化技術等,高度自動化地分析企業的數據,做出歸納性的推理,從中挖掘出潛在的模式,幫助決策者調整市場策略,減少風險,做出正確的決策。
數據挖掘領域10大挑戰性問題:
1.Developing a Unifying Theory of Data Mining
2.Scaling Up for High Dimensional Data/High Speed Streams
3.Mining Sequence Data and Time Series Data
4.Mining Complex Knowledge from Complex Data
5.Data Mining in a Network Setting
6.Distributed Data Mining and Mining Multi-agent Data
7.Data Mining for Biological and Environmental Problems
8.Data-Mining-Process Related Problems
9.Security, Privacy and Data Integrity
10.Dealing with Non-static, Unbalanced and Cost-sensitive Data (非靜態、非平衡及成本敏感數據的挖掘)
鏈接:http://www.cnblogs.com/janemores/archive/2013/04/26/3045962.html