8.txt

This complete matlab for neural network

发信人: yaomc (白头翁&山东大汉), 信区: DataMining
标  题: [合集]非平凡过程？
发信站: 南京大学小百合站 (Thu Jan 17 19:32:16 2002), 站内信件

armen (理性疯狂) 于Fri Dec 28 21:23:08 2001提到：

数据挖掘是从巨量数据中产生有效的、新颖的、潜在有用的、最终可理解的
模式的非平凡过程。
      ~~~~~~~~~~~~~什么叫做非平凡过程啊？什么又是平凡过程？
这词怎么来的？


joe (孔明/3) 于Fri Dec 28 21:42:46 2001提到：

就是说你要挖出来，而不是捡出来。


armen (理性疯狂) 于Fri Dec 28 21:55:37 2001提到：

懂你的意思了，有更详细的解释么？我多了解一点非平凡这个概念



roamingo (漫步鸥) 于Fri Dec 28 22:26:30 2001提到：

"A DM algothim has many loops and very complicated data structure."  
Is this another interpretation for non-trivial?



daniel (飞翔鸟) 于Fri Dec 28 22:40:55 2001提到：


non-trivial, i.e. not easy



yaomc (白头翁&山东大汉) 于Sat Dec 29 09:48:36 2001提到：

I think the nontrivial perhaps means that process of DM does not equal the 
simply query through the database to seek knowledge, it need more steps 
and more skills.


helloboy (hello) 于Sat Dec 29 10:37:09 2001提到：

I think non-trival means datamining needs iterative process.
Preprocess,mining and evaluate patterns are iterative.
When we found out some patterns,perhaps the patterns are not
so good.So we need to preprocess again,mine again and evaluate
again.Until the result can satisfy us.



armen (理性疯狂) 于Sat Dec 29 20:55:11 2001提到：

i think your interpretation is the same to joe's

i want to know what  the non-trivial process means in maths

does it just mean a not-easy process?



explorer (void) 于Sun Dec 30 10:06:49 2001提到：

我的理解是数决挖掘过程不是线性的，不是从开始一直向下走到结束。
在挖掘过程中有反复，有循环，有跳转，而且这种反复和循环和跳转是没有规律的。
仅供参考。


yaomc (白头翁&山东大汉) 于Sun Dec 30 11:19:57 2001提到：

平凡的东西是很容易得到的,也比较浅显,或者说是可以比较准确的预测的。
获得此类的知识不需要太多的技巧和应用专门的工具，只要对于此领域比较熟悉，
能够熟练的预测事物的发展趋势。
而非平凡则是相对于平凡来说的。数据挖掘有时候强调的是，所挖掘的知识
往往不易通过简单的分析就能够得到，这些知识可能隐含在表面现象的内里，
需要经常大量数据的比较分析，应用一些专门对付大数据量的工具，才有可能得到。
得到的知识往往具有出乎意料的意味，因此也往往是不容易预测到的，当然，
数据挖掘得到的知识也用于对事物趋势的预测。
有时候数据挖掘的目的是发现那些出现概率比较小的现象，这些东西好像用一般
统计的方法往往很难获得。

所以，俺认为数据挖掘得到的非平凡知识就是那些往往出乎预料的东西。不是那些领导


fervvac (高远) 于Sun Dec 30 15:51:10 2001提到：

My understanding for this terms is somewhat similar to yours:

trivial means sth. that can be easily known, obtained, etc. 

Before DM came into play, peoples had already begun some analysis of the hist
oric data, but mostly by using some naive methods (like counting, drawing cur
ves, etc) or some basic statistical methods (finding the distribution, cross 
validatation). I guess 
those methods are called trivial in the DM context.

So what DM methods are trying to do is a step further. For example, for the s
tock data, previously we can only draw the curves, try to predict what's the 
trend solely by the experience of the analyst, but with DM techniques, we mig
ht do it more 
accurately and probably more scientifically, :-)

比较浅显,或者说是可以比较准确的预测的。