1 / 5

CS490D: Introduction to Data Mining Prof. Chris Clifton

CS490D: Introduction to Data Mining Prof. Chris Clifton. April 19, 2004 More on Associations/Rules. Project: Association. Several people have suggested association rule mining Good idea Issues Most data is not “itemsets”, but scaled values Is apriori the right algorithm? Ideas

cyndi
Télécharger la présentation

CS490D: Introduction to Data Mining Prof. Chris Clifton

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CS490D:Introduction to Data MiningProf. Chris Clifton April 19, 2004 More on Associations/Rules

  2. Project: Association • Several people have suggested association rule mining • Good idea • Issues • Most data is not “itemsets”, but scaled values • Is apriori the right algorithm? • Ideas • Try Tertius • Decision rules (need to define class)

  3. Tertius • First-order logic learning • Similar to ILP we have been discussing • Provide structural schema definition • Individual: What is an individual (for counting purposes) • Structural: Define relationships between individuals • Properties: Things that describe an individual • http://www.cs.bris.ac.uk/Research/MachineLearning/Tertius/index.html

  4. Tertius: Use • Call: Specify number of literals, number of variables in rules • Finds strongest k rules • Predicate File • Parent 2 person person cwa • Daughter 2 person person cwa • Male 1 person cwa • Fact File • Parent(Chris, Denise) • Daughter(Denise, Chris) • Female(Denise)

  5. JRIP/Ripper • Decision Rules • Like association rules • But need target class (right hand side) • Idea: • Grow rule • Prune rule • If good then keep • Repeat • Growth: Based on information gain • Prune: p+N-n / P+N

More Related