Matches in SemOpenAlex for { <https://semopenalex.org/work/W1518718261> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W1518718261 abstract "Efficient algorithms for mining frequent itemsets are crucial for mining association rules and for other data mining tasks. Methods for mining frequent itemsets and for iceberg data cube computation have been implemented using a prefix-tree structure, known as a FP-tree, for storing compressed frequency information. Numerous experimental results have demonstrated that these algorithms perform extremely well. In this thesis we present a novel FP-array technique that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FP-tree based algorithms. The technique works especially well for sparse datasets. We then present new algorithms for mining all frequent itemsets, maximal frequent itemsets, and closed frequent item-sets. The algorithms use the FP-tree data structure in combination with the FP-array technique efficiently, and incorporate various optimization techniques. In the algorithm for mining maximal frequent itemsets, a variant FP-tree data structure, called a MFI-tree, and an efficient maximality-checking approach are used. Another variant FP-tree data structure, called a CFI-tree, and an efficient closedness-testing approach are also given in the algorithm for mining closed frequent itemsets. Experimental results show that our methods outperform the existing methods in not only the speed of the algorithms, but also their memory consumption and their scalability. We also notice that most algorithms for mining frequent itemsets assume that the main memory is large enough for the data structures used in the mining, and very few efficient algorithms deal with the cases when the database is very large or the minimum support is very low. We thus investigate approaches to mining frequent itemsets when data structures are too large to fit in main memory. Several divide-and-conquer algorithms are presented for mining from disks. Many novel techniques are introduced. Experimental results show that the techniques reduce the required disk accesses by orders of magnitude, and enable truly scalable data mining." @default.
- W1518718261 created "2016-06-24" @default.
- W1518718261 creator A5005714968 @default.
- W1518718261 creator A5050600383 @default.
- W1518718261 date "2004-01-01" @default.
- W1518718261 modified "2023-09-23" @default.
- W1518718261 title "Efficiently mining frequent itemsets from very large databases" @default.
- W1518718261 hasPublicationYear "2004" @default.
- W1518718261 type Work @default.
- W1518718261 sameAs 1518718261 @default.
- W1518718261 citedByCount "2" @default.
- W1518718261 crossrefType "dissertation" @default.
- W1518718261 hasAuthorship W1518718261A5005714968 @default.
- W1518718261 hasAuthorship W1518718261A5050600383 @default.
- W1518718261 hasConcept C113174947 @default.
- W1518718261 hasConcept C11413529 @default.
- W1518718261 hasConcept C124101348 @default.
- W1518718261 hasConcept C13280743 @default.
- W1518718261 hasConcept C134306372 @default.
- W1518718261 hasConcept C162319229 @default.
- W1518718261 hasConcept C163797641 @default.
- W1518718261 hasConcept C176809094 @default.
- W1518718261 hasConcept C190290938 @default.
- W1518718261 hasConcept C193524817 @default.
- W1518718261 hasConcept C197855036 @default.
- W1518718261 hasConcept C199360897 @default.
- W1518718261 hasConcept C205649164 @default.
- W1518718261 hasConcept C33923547 @default.
- W1518718261 hasConcept C41008148 @default.
- W1518718261 hasConcept C48044578 @default.
- W1518718261 hasConcept C77088390 @default.
- W1518718261 hasConcept C78168278 @default.
- W1518718261 hasConceptScore W1518718261C113174947 @default.
- W1518718261 hasConceptScore W1518718261C11413529 @default.
- W1518718261 hasConceptScore W1518718261C124101348 @default.
- W1518718261 hasConceptScore W1518718261C13280743 @default.
- W1518718261 hasConceptScore W1518718261C134306372 @default.
- W1518718261 hasConceptScore W1518718261C162319229 @default.
- W1518718261 hasConceptScore W1518718261C163797641 @default.
- W1518718261 hasConceptScore W1518718261C176809094 @default.
- W1518718261 hasConceptScore W1518718261C190290938 @default.
- W1518718261 hasConceptScore W1518718261C193524817 @default.
- W1518718261 hasConceptScore W1518718261C197855036 @default.
- W1518718261 hasConceptScore W1518718261C199360897 @default.
- W1518718261 hasConceptScore W1518718261C205649164 @default.
- W1518718261 hasConceptScore W1518718261C33923547 @default.
- W1518718261 hasConceptScore W1518718261C41008148 @default.
- W1518718261 hasConceptScore W1518718261C48044578 @default.
- W1518718261 hasConceptScore W1518718261C77088390 @default.
- W1518718261 hasConceptScore W1518718261C78168278 @default.
- W1518718261 hasOpenAccess W1518718261 @default.
- W1518718261 hasRelatedWork W1506729058 @default.
- W1518718261 hasRelatedWork W2025769208 @default.
- W1518718261 hasRelatedWork W2034210139 @default.
- W1518718261 hasRelatedWork W2079961873 @default.
- W1518718261 hasRelatedWork W2100303650 @default.
- W1518718261 hasRelatedWork W2125231721 @default.
- W1518718261 hasRelatedWork W2151953639 @default.
- W1518718261 hasRelatedWork W2164423284 @default.
- W1518718261 hasRelatedWork W22068932 @default.
- W1518718261 hasRelatedWork W2550563931 @default.
- W1518718261 hasRelatedWork W2779938839 @default.
- W1518718261 hasRelatedWork W2790530433 @default.
- W1518718261 hasRelatedWork W2809763994 @default.
- W1518718261 hasRelatedWork W2893242804 @default.
- W1518718261 hasRelatedWork W2901273528 @default.
- W1518718261 hasRelatedWork W3008639473 @default.
- W1518718261 hasRelatedWork W77492236 @default.
- W1518718261 hasRelatedWork W841472321 @default.
- W1518718261 hasRelatedWork W2607588219 @default.
- W1518718261 hasRelatedWork W926614039 @default.
- W1518718261 isParatext "false" @default.
- W1518718261 isRetracted "false" @default.
- W1518718261 magId "1518718261" @default.
- W1518718261 workType "dissertation" @default.