The above term means "revenue generation through (data) mining services".

If this translation is to be used as the title of a course, you may use "The application of data mining in revenue generation".

"挖掘" in marketing or management information systems (MIS) means "mining", probably "data mining" although it is not explicitly stated here.

'创收" means "revenue generation" or "income generation".

Personally, I think the term "深度" is redundant here.


Data mining is a new component in an enterprise's decision support system (DSS) architecture. It complements and interlocks with other DSS capabilities such as query and reporting, on-line analytical processing (OLAP), data visualization, and traditional statistical analysis. Shown in Figure 1, these other DSS technologies are generally retrospective. They provide reports, tables, and graphs of what happened in the past. A user who knows what she's looking for can answer specific questions like: "How many new accounts were opened in the Midwest region last quarter," "Which stores had the largest change in revenues compared to the same month last year," or "Did we meet our goal of a ten-percent increase in holiday sales?"

A data mining pilot project can provide important insight into the fields and aggregates that need to be designed into the warehouse to make it really valuable. Further, the cost savings or revenue generation provided by data mining can provide bootstrap funding for a data warehouse or related initiatives.

http://www.tdan.com/view-articles/5263/


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Note added at 3 hrs (2010-01-30 16:20:01 GMT)
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Why does data mining matter?

Revenue generation by targeted advertising/marketing
Retailing, banking, insurance, real estate
Business intelligence
Quality control
Manufacturing, hospital, services, retail
Fraud detection
Insurance, credit card, auditing, healthcare, government
agencies, homeland security

http://74.125.153.132/search?q=cache:vkEHiFjmlC4J:www.likes....


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Note added at 3 hrs (2010-01-30 16:23:49 GMT)
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数据挖掘（英語：Data mining），又譯為資料採礦、資料探勘。它是資料庫知識發現（英語：Knowledge-Discovery in Databases，簡稱：KDD)中的一個步驟。数据挖掘一般是指從大量的資料中自動搜索隱藏於其中的有着特殊關聯性（屬於Association rule learning）的信息的過程。資料挖掘通常與電腦科學有關，並通過統計、在线分析处理、情報檢索、機器學習、專家系統（依靠過去的經驗法則）和模式識別等諸多方法來實現上述目標。
http://zh.wikipedia.org/wiki/数据挖掘

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Note added at 16 hrs (2010-01-31 05:50:23 GMT)
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Or you may also try "Revenue generation through the depth of (data) mining".

http://www.google.com/#hl=en&q="depth of data mining"&aq=f&a...