| ISBN: 3-540-25878-7
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| |
| description |
Some data mining tasks can produce such great amounts of data that
we have to cope with a new knowledge management problem. Frequent
itemset mining fits in this category. Different approaches were
proposed to handle or avoid somehow this problem. All of them have
problems and limitations. In particular, most of them need the
original data during the analysis phase, which is not feasible for
data streams. The DWFIST (Data Warehouse of Frequent ItemSets
Tactics) approach aims at providing a powerful environment for the
analysis of itemsets and derived patterns, such as association
rules, without accessing the original data during the analysis
phase. This approach is based on a Data Warehouse of Frequent
Itemsets. It provides frequent itemsets in a flexible and efficient
way as well as a standardized logical view upon which analytical
tools can be developed. This paper presents how such a data
warehouse can be built.
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| publisher |
Springer
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| type |
Text
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| Article in Proceedings
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| source |
In: Proceedings of the 15th International Symposium on Methodologies
for Intelligent Systems Saratoga Springs, New York - May 25-28,
2005, pp. 1-9
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| contributor |
IPVS, Anwendersoftware
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| subject |
Database Administration (CR H.2.7)
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| Database Applications (CR H.2.8)
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| relation |
Lecture Notes in Computer Science; 3488
|