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数据挖掘

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发表于 2009-1-26 20:06:54 | 显示全部楼层 |阅读模式
大家可以讨论一下这个方向怎么样?
发表于 2009-1-26 21:48:39 | 显示全部楼层
no book? only talk?
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发表于 2009-1-26 21:04:28 | 显示全部楼层
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发表于 2009-9-28 16:23:24 | 显示全部楼层
这方面的书比较多,经典的是韩家炜编写的:数据挖掘 概念与技术
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发表于 2009-9-30 06:42:55 | 显示全部楼层
楼主是这个专业的啊
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发表于 2009-10-6 10:48:53 | 显示全部楼层
有没有现实中比较成熟的应用软件?
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发表于 2009-10-9 01:21:48 | 显示全部楼层
数据挖掘是个大方向,具体点吧
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发表于 2009-10-9 01:22:29 | 显示全部楼层
What can data mining do?
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data.

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments
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发表于 2009-10-9 01:23:11 | 显示全部楼层
What technological infrastructure is required?
Today, data mining applications are available on all size systems for mainframe, client/server, and PC platforms. System prices range from several thousand dollars for the smallest applications up to $1 million a terabyte for the largest. Enterprise-wide applications generally range in size from 10 gigabytes to over 11 terabytes. NCR has the capacity to deliver applications exceeding 100 terabytes. There are two critical technological drivers:

Size of the database: the more data being processed and maintained, the more powerful the system required.
Query complexity: the more complex the queries and the greater the number of queries being processed, the more powerful the system required.
Relational database storage and management technology is adequate for many data mining applications less than 50 gigabytes. However, this infrastructure needs to be significantly enhanced to support larger applications. Some vendors have added extensive indexing capabilities to improve query performance. Others use new hardware architectures such as Massively Parallel Processors (MPP) to achieve order-of-magnitude improvements in query time. For example, MPP systems from NCR link hundreds of high-speed Pentium processors to achieve performance levels exceeding those of the largest supercomputers.
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发表于 2009-10-9 01:23:41 | 显示全部楼层
How does data mining work?
While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:

Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.
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