Travelled to:1 × Austria
1 × India
1 × South Korea
2 × Canada
2 × USA
Collaborated with:Y.Morimoto T.Tokuyama S.Morishita K.Yoda K.Yoshimura Y.Atarashi H.Matsuzawa W.Li D.Gao R.Bhatti I.Narang M.Numao M.Ohkawa
Talks about:rule (6) associ (5) optim (5) mine (4) numer (3) algorithm (2) featur (2) data (2) use (2) rectilinear (1)
Person: Takeshi Fukuda
DBLP: Fukuda:Takeshi
Contributed to:
2010
20071998
1997
1996
19961996Wrote 8 papers:
- SPLC-2010-YoshimuraAF #constraints #feature model #identification #mining
- A Method to Identify Feature Constraints Based on Feature Selections Mining (KY, YA, TF), pp. 425–429.
- VLDB-2007-LiGBNMNOF
- Deadline and QoS Aware Data Warehouse (WSL, DG, RB, IN, HM, MN, MO, TF), pp. 1418–1421.
- VLDB-1998-MorimotoFMTY #algorithm #category theory #database #mining
- Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases (YM, TF, HM, TT, KY), pp. 380–391.
- KDD-1997-YodaFMMT
- Computing Optimized Rectilinear Regions for Association Rules (KY, TF, YM, SM, TT), pp. 96–103.
- PODS-1996-FukudaMMT #mining
- Mining Optimized Association Rules for Numeric Attributes (TF, YM, SM, TT), pp. 182–191.
- SIGMOD-1996-FukudaMMT #2d #algorithm #data mining #mining #using #visualisation
- Data Mining Using Two-Dimensional Optimized Accociation Rules: Scheme, Algorithms, and Visualization (TF, YM, SM, TT), pp. 13–23.
- SIGMOD-1996-FukudaMMT96a #named
- SONAR: System for Optimized Numeric AssociationRules (TF, YM, SM, TT), p. 553.
- VLDB-1996-FukudaMMT #performance #using
- Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules (TF, YM, SM, TT), pp. 146–155.