lecture8

# lecture8 - Data Mining CS57300 Purdue University Predictive...

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Data Mining CS57300 Purdue University September 21, 2010

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Predictive modeling: learning
Learning algorithms • Scoring function • Search technique

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Search
”…an agent with several immediate options of unknown value can decide what to do by Frst examining different possible sequences of actions that lead to states of known value, and then choosing the best sequence." (Russell & Norvig, 2002)

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Problem solving as search • Search = Exploration of possibilities • Abstraction of general problem solving method • Basic ingredients: • States: describe conFguration of environment • Actions: activities which move you from one state to another • Search algorithm: determines which actions should be tried in which order to Fnd solution
Cannibal and Missionaries problem • Three cannibals and three missionaries come to a crocodile infested river. • There is a boat on their side that can be used by either one or two persons. • If cannibals outnumber the missionaries at any time, the cannibals eat the missionaries. • How can they use the boat to cross the river so that all missionaries survive?

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Problem formulation • What is the goal to be achieved? • What are the actions? • What is the state space?
Search algorithms • Generate a search tree by: • Considering a particular state • Testing to see if it is the goal state • And if not, expanding the node to generate successor states (by applying all possible actions) • Search strategies differ in their choice of which state to expand

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Local search • Used when path to goal state does not matter • Basic idea: • Only use current state • Don’t save paths followed • Why use local search? • Low memory requirements: Usually constant • Effective: Can often Fnd good solutions in extremely large state spaces
• Place eight queens on a chessboard so that no queen can attack another

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lecture8 - Data Mining CS57300 Purdue University Predictive...

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