网站大量收购独家精品文档,联系QQ:2885784924

DATA WAREHOUSING AND DATA MINING - PSU - Intranet数据仓库和数据挖掘-电源-网.ppt

DATA WAREHOUSING AND DATA MINING - PSU - Intranet数据仓库和数据挖掘-电源-网.ppt

  1. 1、本文档共302页,可阅读全部内容。
  2. 2、有哪些信誉好的足球投注网站(book118)网站文档一经付费(服务费),不意味着购买了该文档的版权,仅供个人/单位学习、研究之用,不得用于商业用途,未经授权,严禁复制、发行、汇编、翻译或者网络传播等,侵权必究。
  3. 3、本站所有内容均由合作方或网友上传,本站不对文档的完整性、权威性及其观点立场正确性做任何保证或承诺!文档内容仅供研究参考,付费前请自行鉴别。如您付费,意味着您自己接受本站规则且自行承担风险,本站不退款、不进行额外附加服务;查看《如何避免下载的几个坑》。如果您已付费下载过本站文档,您可以点击 这里二次下载
  4. 4、如文档侵犯商业秘密、侵犯著作权、侵犯人身权等,请点击“版权申诉”(推荐),也可以打举报电话:400-050-0827(电话支持时间:9:00-18:30)。
查看更多
DATA WAREHOUSING AND DATA MINING - PSU - Intranet数据仓库和数据挖掘-电源-网

* * * * * * * Absolute: 40 M$ 40M$, expected to grow 10 times by 2000 --Forrester research * * Littl e integration: here are few exceptions People are starting to wake up to this possibility and here are some examples I have found by web-surfing. . decision tree most common. Information Discovery claimed to be only serious integrator [DBMS Ap ‘98] Clustering used by some to define new product hierarchies. Of course, rich set of time-series functions especially for forecasting was always there New charting software: 80/20, A-B-C analysis, quadrant plotting. Univ. Jiawen Han. Previous approach has been to bring in mining operations in olap. Look at mining operations and choose what fits. My approach has been to reflect on what people do with cube metaphor and the drill-down, roll-up, based exploration and see if there is anything there that can be automated. Discuss my work first. * * Angoss Software Corp. was formed under the Business Corp. Act (ontario) in 1980. It began data mining software production in 1992. It is publicly traded on the Canadian Venture Exchange under the trading symbol “ANC” Promote the rapid knowledge transfer to customers in the use of technology and adoption of “best practice” for data mining * * * * * * * * Here it’s obviously the algorithms. * Here it’s less clear – maybe it’s the algorithms, but more it’s the attitude… * * * * * How it works How it’s really used. Handling of business problems and algorithms / expert features Deep embedding of deployment CRISP-DM pane * * In the previous lesson we discussed Classification using decision trees. Sometimes decision trees are inconvenient – they can be very large Also, they require starting at the same attribute We can extract modular “nuggets” of knowledge by getting rules * * Classification: Train, Validation, Test split Data Predictions Y N Results Known Training set Validation set + + - - + Model Builder Evaluate + - + - Final Model Final Test Set + - + - Final Evaluatio

文档评论(0)

153****9595 + 关注
实名认证
内容提供者

该用户很懒,什么也没介绍

1亿VIP精品文档

相关文档