High Performance Data Mining: Scaling Algorithms, Applications and SystemsYike Guo, R.L. Grossman Springer Science & Business Media, 08.05.2007 - 106 Seiten High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area. High Performance Data Mining: Scaling Algorithms, Applications and Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field. |
Inhalt
Abschnitt 1 | 20 |
Abschnitt 2 | 29 |
Abschnitt 3 | 49 |
Abschnitt 4 | 51 |
Abschnitt 5 | 52 |
Abschnitt 6 | 76 |
Abschnitt 7 | 97 |
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High Performance Data Mining: Scaling Algorithms, Applications and Systems Yike Guo,R.L. Grossman Eingeschränkte Leseprobe - 1999 |
High Performance Data Mining: Scaling Algorithms, Applications and Systems Yike Guo,R.L. Grossman Keine Leseprobe verfügbar - 2013 |
High Performance Data Mining: Scaling Algorithms, Applications and Systems Yike Guo,R.L. Grossman Keine Leseprobe verfügbar - 1999 |
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Agrawal and Srikant algorithm for mining allocation batch candidate sets chordal chordal graph class distribution information classification tree cluster found w.r.t. cluster w.r.t. communication cost communication overhead continuous attributes core point count at processor data mining data skewness dataset DBSCAN decision tree density-connected directly density-reachable domain dR*-tree efficient Eps and MinFts evaluation explorer Figure gl-large at processor global pruning graph G halt signal hash table Hilbert curves ifdh implemented large itemsets LJXUH load balancing load imbalance Markov network MC(C merging candidates MIMD MinPts number of processors parallel algorithms parallel clustering algorithm Parallel Computing parallel formulations PartDBSCAN partitioned tree construction PDBSCAN performance pruning techniques query received graph Robert Grossman run-time scalable scaleup Section sizeup skewness and workload ſº space constraint DB spatial access methods spatial databases speedup split strategy subset support counts training data tree construction approach tuples valid graphs w.r.t. the space workload balance