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Clustering algorithms group you with like minds.
Use multi-dimensional clustering (see a comp sci geek for more information) to group people having similar tastes and tendencies. As an example, multi-dimensional clustering allows you to analyze thousands of shoppers and classify individuals according to whatever attributes turn out to be the primary
identifying traits: single guys in their thirties who shop at night; minorites who always buy produce; old ladies who buy everything on sale.
The ClusterPals clusters would not be definable by any particular attribute (as in USENET newsgroups, where "fans of woody guthrie" might be the only qualifying attribute) but would contain people who are similar, to varying degrees, in a variety of areas. To find your cluster, you would answer a variety of questions much as you do for a Meyers-Briggs classification, except that Meyers-Briggs sorts you only along four known axes. The ClusterPals algorithm could create any number of clusters. There could be a process for adding/changing the interview questions. People could retake the interview & reclassify themselves from time to time if they wished.
[subflower, Aug 06 2005]
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||Does a plumber want to live in a subdivision of carpenters? There are benefits and disadvantages. How far do you want to take it?
But for people that don't have 'foundational' support structure when they should, this could be very important.
I haven't finished my reasoning here or on a recent idea post that I had but anything to stop citizens from becoming astronauts lost in space in their own town/community is good for me.
||Geo referenced web user data can be utilized for this. if your thing is like a window that web visitors browse through then this data can be tracked with the browser history to identify people with similar taste. This could be incorporated into a dynamic view like Grokker has. As much information as you wanted to add to your profile would make the matches more detailed or you could add terms to refine the group cluster.