复旦商务智能概论--4数据挖掘PrinciplesofDM
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Discovery-driven
Computer sifts through millions of hypotheses and only presents the most interesting/valid ones
Example:
From a sample group of clients that have defected to a competitive bank - identify client characteristics that are strongly correlated, and using these attributes, score the rest of the client and prospect population and the strength of their relationships to sample group.
有用的
数据挖掘过程可能会传递正确的和重要的结果， 但是这些知识必须是对商业有用的。如结果告诉 你要在一个大量的渠道上多样化市场运作，这可 能会无法办到。同样结果必须使你能抢在竞争对 手之前行动。
未知的
数据挖掘要产生新的信息。如果过程只是传递 一些无关紧要的结果，那么数据挖掘的商业动 力就会消失。这就是区分验证和探索的性质。
数据挖掘受多学科的影响
数据挖掘是一 个交叉科学领 域，受多个学 科影响，包括 数据库系统、 统计、机器学 习、可视化和 信息科学。
一个比较正式的数据挖掘的定义
高层次上的主动式自动发现方法，被称为发现驱动型知识发现。 从数据中提取正确的、有用的、未知的和综合的信息并用它进
行决策的过程。 数据挖掘的相关学科是统计理论、数据库技术和人工智能。 前Business Objects的Todd Rowe曾表示：“从技术上讲，甚至
Verification-driven data mining tools extract data. The user is expected to generate information based on his interpretation of the returned data.
New Process With Data Mining
personalization in e-commerce Data mining has become a part of business function in many
companies
Data mining is regularly used in
典型的数据挖掘系统结构
Verification-Driven Analysis
数据挖掘原理
博士Байду номын сангаас
What is Data Mining?
According to the Gartner Group, Data mining is the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques.
只要有完备的Excel数据就能用上BI。”
过程
数据挖掘并不是一个装在软件包装盒中的工具 可以简单的买到并运行在商业智能环境中，也 不会自动开始产生值得注意的商业规律。
正确的
提取的信息应该是正确的，并且在统计上是重 要的以支持有依据的决定。正确意味着确证性 和完整性。不但需要从数据库中得到正确的客 户，还希望得到所有正确的客户。这就需要原 始数据和数据挖掘过程都具有正确性。
Data mining refers to the work of discovering new and useful
(business) knowledge from large real databases through a non-trivial process and using a sound methodology and multiple data processing and analytical techniques. Examples:
Detect taxation fraud: not declaring all income for taxation; From the thousands of mobile phone customers, predict which
customers are going to switch to a competitor .
最小要求
以上显示了数据挖掘最小要求，可以用它来评 价数据挖掘是否对业务环境增加了附加的价值
其他要求
Why Data Mining?
Gain an insight into business data
Identify useful patterns, correlations and models from data automatically to answer questions like, Which customer is likely to churn in two months? Which customer is my cross sell target? What are the characteristics of my high spending and low spending customers?
Data mining is a core technology of business intelligence Data mining is a core application of data warehouses Data mining is the core technology of analytical CRM Data mining is the core technology of online recommendation and
Computer sifts through millions of hypotheses and only presents the most interesting/valid ones
Example:
From a sample group of clients that have defected to a competitive bank - identify client characteristics that are strongly correlated, and using these attributes, score the rest of the client and prospect population and the strength of their relationships to sample group.
有用的
数据挖掘过程可能会传递正确的和重要的结果， 但是这些知识必须是对商业有用的。如结果告诉 你要在一个大量的渠道上多样化市场运作，这可 能会无法办到。同样结果必须使你能抢在竞争对 手之前行动。
未知的
数据挖掘要产生新的信息。如果过程只是传递 一些无关紧要的结果，那么数据挖掘的商业动 力就会消失。这就是区分验证和探索的性质。
数据挖掘受多学科的影响
数据挖掘是一 个交叉科学领 域，受多个学 科影响，包括 数据库系统、 统计、机器学 习、可视化和 信息科学。
一个比较正式的数据挖掘的定义
高层次上的主动式自动发现方法，被称为发现驱动型知识发现。 从数据中提取正确的、有用的、未知的和综合的信息并用它进
行决策的过程。 数据挖掘的相关学科是统计理论、数据库技术和人工智能。 前Business Objects的Todd Rowe曾表示：“从技术上讲，甚至
Verification-driven data mining tools extract data. The user is expected to generate information based on his interpretation of the returned data.
New Process With Data Mining
personalization in e-commerce Data mining has become a part of business function in many
companies
Data mining is regularly used in
典型的数据挖掘系统结构
Verification-Driven Analysis
数据挖掘原理
博士Байду номын сангаас
What is Data Mining?
According to the Gartner Group, Data mining is the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques.
只要有完备的Excel数据就能用上BI。”
过程
数据挖掘并不是一个装在软件包装盒中的工具 可以简单的买到并运行在商业智能环境中，也 不会自动开始产生值得注意的商业规律。
正确的
提取的信息应该是正确的，并且在统计上是重 要的以支持有依据的决定。正确意味着确证性 和完整性。不但需要从数据库中得到正确的客 户，还希望得到所有正确的客户。这就需要原 始数据和数据挖掘过程都具有正确性。
Data mining refers to the work of discovering new and useful
(business) knowledge from large real databases through a non-trivial process and using a sound methodology and multiple data processing and analytical techniques. Examples:
Detect taxation fraud: not declaring all income for taxation; From the thousands of mobile phone customers, predict which
customers are going to switch to a competitor .
最小要求
以上显示了数据挖掘最小要求，可以用它来评 价数据挖掘是否对业务环境增加了附加的价值
其他要求
Why Data Mining?
Gain an insight into business data
Identify useful patterns, correlations and models from data automatically to answer questions like, Which customer is likely to churn in two months? Which customer is my cross sell target? What are the characteristics of my high spending and low spending customers?
Data mining is a core technology of business intelligence Data mining is a core application of data warehouses Data mining is the core technology of analytical CRM Data mining is the core technology of online recommendation and