Internet as Database.. Keeps Comming up. Scrape+MachinePatternRecog+$Nodes

Wow, this shit really hit home. I mean. its everything I’ve had in my head.
1) A tool for visual Artists.. that scrape and build NODEs
2) Mashing up the Data with other Data to build relationships?
3) Presenting this NEW view to a “collective” via “web services”
4) Selling VIEW constructors and creating VALUE based on perspectives or DataRelationships

A) Could you build a scape of the Last 100 most view’d producers on youtube and MIX with….

look for patterns?

B) Take the Top 50 sites for specific keywords?
— MicroMarkets. and Encapsulize them into MiniFeeds/Widgets?

i dunno I’m rambling… ARGG

<b>Go beyond simple database-backed applications and put the wealth of Internet data to work for you. </b>

Book Description
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it.

Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general — all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:

clipped from

Building Smart Web 2.0 Applications

  • Collaborative filtering techniques that enable online retailers to recommend products or media
  • Methods of clustering to detect groups of similar items in a large dataset
  • Search engine features — crawlers, indexers, query engines, and the PageRank algorithm
  • Optimization algorithms that search millions of possible solutions to a problem and choose the best one
  • Bayesian filtering, used in spam filters for classifying documents based on word types and other features
  • Using decision trees not only to make predictions, but to model the way decisions are made
  • Predicting numerical values rather than classifications to build price models
  • Support vector machines to match people in online dating sites
  • Non-negative matrix factorization to find the independent features in a dataset
  • Evolving intelligence for problem solving — how a computer develops its skill by improving its own code the more it plays a game
Programming Collective Intelligence: Building Smart Web 2.0 Applications (Paperback)
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