Transcription of 頻出パターンマイニング - kamishima.net
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頻出パターンマイニング - kamishima.net
Frequent Pattern Mining . 1.. 2.. Association Rule 3.. Association Rule X (antecedent). X Y Y (consequent). X Y . ( ). X Y .. { , } { }. (X ) . (Y ) . 2. 4.. X i . X i . X = { , }. T1 = { , , }. T2 = { , }. X 1 1 . X 2 2 .. X Y . 5.. X Y . { , } { }. { , } { }.. , , , .. 6.. X Y . { } { }.. X Y . { } { }.. 7.. Basket Data ( ) .. T1 = { , , }. T2 = { , }.. TN = { , }. T1 . ( ) .. 8.. support(X). X .. X {a, b} . T1 = {a, b, c} T2 = {a, d}. T3 = {b, d, e} T4 = {a, b, e}. T5 = {a, b, c} T6 = {d, e}. = 6. ( ) = 3. X 3. support(X) = . = 6. = 9.. con dence(X,Y). X X . Y . X Y . con dence(X,Y) =. X . X Y X Y . X Y Ti support(X Y). con dence(X,Y) =. support(X). 10. Apriori 11. Apriori Apriori .. and "Fast Algorithms for Mining Association Rules", VLDB 1994.. minsup minconf . X Y . support(X Y) minsup con dence(X,Y) minconf 12. Apriori : .. ! 10 . 57,002 .. ! . 13. Apriori Step 1.. X Y . support(X Y) support(X).
頻出パターンマイニング 2 頻出パターン データ集合の要素アイテム集合，系列データ，時系列，木，グラフ…
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