Statements (31)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
data mining algorithm
frequent pattern mining algorithm |
| gptkbp:advantage |
efficient for large datasets
does not require candidate generation |
| gptkbp:alternativeTo |
gptkb:Eclat_algorithm
|
| gptkbp:category |
unsupervised learning
pattern mining |
| gptkbp:citation |
over 10,000
|
| gptkbp:complexity |
linear with respect to number of transactions
|
| gptkbp:developedBy |
gptkb:Jiawei_Han
|
| https://www.w3.org/2000/01/rdf-schema#label |
FP-Growth algorithm
|
| gptkbp:implementedIn |
gptkb:Java
gptkb:Python gptkb:C++ R |
| gptkbp:input |
transaction database
|
| gptkbp:introducedIn |
2000
|
| gptkbp:notablePublication |
Mining Frequent Patterns without Candidate Generation (Han et al., 2000)
|
| gptkbp:openSource |
MLlib (Apache Spark)
Orange Data Mining SPMF |
| gptkbp:output |
frequent itemsets
|
| gptkbp:publishedIn |
gptkb:SIGMOD_2000
|
| gptkbp:relatedTo |
gptkb:Apriori_algorithm
|
| gptkbp:requires |
minimum support threshold
|
| gptkbp:step |
build FP-tree
mine FP-tree recursively |
| gptkbp:usedFor |
association rule learning
frequent itemset mining |
| gptkbp:bfsParent |
gptkb:Apriori_algorithm
|
| gptkbp:bfsLayer |
8
|