Radford It is true that the Fuzzy Data mining system that I talked about could do with some improvement but "Fraught with contradiction" makes it sound as if contradicting rules are an undesirable Data Mining outcome. As a mathematics that aspires to deal with A.I. Fuzzy logic's ability to cope with what is a real world phenomena (apparent contradiction) makes it such an interesting tool. I knew nothing about Iris classification before the Fuzzy Data Mining system (in question) took a few minutes to work through some raw data and then explain to me some of the basics about how the plant characteristics related to its type classification. The point is that the data and the chore were boring, the outcomes were exciting, the contradictions providing both linguistic insight and necessary Fuzzy Model shaping rules that assist with predicting the consequent results of combining test data and the previously mined rules. Rob W Radford Neal wrote in message <2001Aug16.113756.2673@jarvis.cs.toronto.edu>...> In article <9lg89e$a0g$1@perki.connect.com.au>, > Wise <weiz@spam-block.sympac.com.au> wrote: > >> Control systems that learn their job on the job are the promise of >> fuzzy learning (or Fuzzy data mining) techniques. >> >> ... >> >> When you devise a system that creates rules from an analysis of >> "raw" data that is part of the workings of a system you find that >> the proliferation of contradictory rules is common place. >> >> An analysis of the Irises database, for example, by many Data Mining >> methods highlights that classification of the plant by analysing >> basic measurements of its component parts is fraught with >> contradiction. > > I've looked at the Iris data, as have many generations of statisticians. > The main problem with this data is that it is so boring. Nothing > interesting is going on. It's easily handled by any number of simple > statistical techniques. If your data mining system produces results > "fraught with contradiction", you need a better data mining system, > not fuzzy logic. > > Radford Neal > > ---------------------------------------------------------------------------
-> Radford M. Neal radford@cs.utoronto.ca > Dept. of Statistics and Dept. of Computer Science
radford@utstat.utoronto.ca> University of Toronto
http://www.cs.utoronto.ca/~radford> ---------------------------------------------------------------------------
-