157.txt

This complete matlab for neural network

发信人: fervvac (高远), 信区: DataMining
标  题: Re: 对大的数据集的处理？
发信站: 南京大学小百合站 (Sat Dec 29 15:00:17 2001), 站内信件

I see. I rremember Padley is a very theoretical guy, his paper must be 
diffciult to read, :-)

For clustering algorithms, I think it can be done in a "progressive" manner. 
I do feel that property important, as most analysis sessions are of interactive
nature (as well as explorative nature). It is thus very important to 
proviide a preliminary result to the analyst as soon as possible.

In fact, I am a newbie to DM field. My research interest is Data Warehousing
and OLAP , XML. But I recently read something on sequence pattern mining and
get some basic knowledge in the DM field, :-)

【 在 roamingo (漫步鸥) 的大作中提到: 】
: 【 在 fervvac (高远) 的大作中提到: 】
: : However, I wonder if there is any algorithm (AR mining) that need only 
: : 1 scan over the data set? Even for FP-Tree based methods (which is believed
: : to be fastest), several scan over the data (might not be the original data
: : set, but the projected DB) is necessary if memory is not enough.
: I agree that it is true in the AR sub-field.
: : And for incremental mining, you cannot stop at any arbitrary moment, can ..
: The word incremental in that paper has probabaly a little different meaning.
: It is said to describe algorithms that needs exactly one scan.  If it can
: output result at any period during the scan, and only quality diffs, we can
: say this algorithm has the incremental feature.  Such algorithms exists,
: especially in the clustering sub-field.
: : I am sorry I didn't read that paper before asking those possibly silly 
: : questions, but I am really busy with other papers, :-( Hope you don't min..
: Absolutely not.  I can't understand that paper much.  The only benefit for me
: after reading it is the desired features for a DM algorithm, just as what I 
: rephrased here before.  Moreover, I believe I am just a newbie in DM.  You ..
: other guys all have very insightful ideas, and I have benefited a lot from 
: your articals.  Let's try our best to make here an invaluable place for DMers.


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