8.txt

This complete matlab for neural network

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jueww (觉·无我) 于Wed Jan 22 10:42:51 2003)
提到：

这是一个比较简洁的高频集综述. 92年-2000年. 

【 在 jueww 的大作中提到: 】
: 高频集发现方法综述
: 高频集发现就是从目标数据库中找出所有支持度大于预先给定的最小支持度


NAOMIELIE (雁来红) 于Wed Jan 22 22:26:43 2003)
提到：

The reference part is a mess, which makes the survey meaningless.

【 在 jueww 的大作中提到: 】
: 高频集发现方法综述
: 高频集发现就是从目标数据库中找出所有支持度大于预先给定的最小支持度
: 的项集。高频集发现在关联规则发现[47]、相关性发现[48]、因果关系发
: 现[49]、序列模式发现[50-52]、episode发现[53-55]、偏周期性发现
: [56-58]、profile规则发现[59]、事务间关联规则发现[60,61]等领域起着
: 关键作用。
: 高频集发现和关联规则发现的关系最为密切。关联规则发现致力于发现满足
: 支持度/可信度要求的关联规则，它分为高频集发现和规则生成两个步骤。
: 由于高频集发现是关联规则发现算法提高性能的瓶颈，所以几乎所有对关联
: 规则算法的研究都致力于在保证精度的基础上提高算法的运行效率，其中精
: 度是指所发现高频集的满足要求的程度。1993年，Agrawal等[47]提出关
: 联规则发现问题，同时提出第一个高频集发现算法。此后，在各种问题背景
: 下，围绕着提高算法效率和结果的有用性（即用户对其感兴趣程度），研究
: 者们提出各种高频集发现算法。根据这些算法的研究重点不同，可将其分为
: 基本高频集发现算法和增强高频集发现算法。前者致力于设计各种算法框
: 架，高效地发现所有支持度大于某个不变的最小支持度的高频集。后者致力
: 于提高发现结果的有用性。基本高频集发现算法的结果往往不能满足用户要
: 求，比如所发现的高频集的有用性不高、发现的高频集数量过多、遗漏用户
: 感兴趣的高频集等等，增强高频集发现算法通过引入概念层次结构、约束条
: 件、可变支持度等方式克服这些缺陷。
: (以下引言省略...)


NAOMIELIE (雁来红) 于Thu Jan 23 00:02:30 2003)
提到：

Here is a survey paper from SIGKDD Exploration, 2000:
http://www.acm.org/sigs/sigkdd/explorations/issue2-1/hipp.pdf

Han, Lakes, and Pei gave a tutorial on kdd'01:
http://www.cse.buffalo.edu/faculty/jianpei/publications/kdd01tutorial.zip

【 在 vant 的大作中提到: 】
: 3x！


jueww (觉·无我) 于Thu Jan 23 08:43:54 2003)
提到：

hipp的比较很不完全.
han的不错, 不过我写这篇综述的时候还没有看到它的这篇tutorial.否则肯定省下不少时
间.
我综述的内容和它的差不多都相同, 可能我的多提到些采样/PARTITION算法之类吧.由于篇
幅关系, 没提到HAN中讲到的sequential pattern mining(这个也属于应用)和应用.

我的综述中的文献编号减去一个常数就是参考文献给出的编号.

【 在 NAOMIELIE 的大作中提到: 】
: Here is a survey paper from SIGKDD Exploration, 2000:
: http://www.acm.org/sigs/sigkdd/explorations/issue2-1/hipp.pdf
: 
: Han, Lakes, and Pei gave a tutorial on kdd'01:
: http://www.cse.buffalo.edu/faculty/jianpei/publications/kdd01tutorial.zip
: 
: 【 在 vant 的大作中提到: 】