R. Srikant. Fast algorithms for mining association rules and sequential patterns. PhD thesis, University of Wisconsin, Madison, 1996. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Expert-Driven Validation of Set-Based Data Mining Results - Adomavicius (2002) (Correct)
....set. As the result, the running time of a rule aggregation (lines 5 6) is linear in the size of the rule (i.e. total number of attributes in the body and the head of the rule) Also, since the group set GroupSet is implemented as a hash tree data structure (similar to the one described by [75]) an insertion of a group into the resulting group set G (line 7) is also linear in the size of the rule. Consequently, the running time of the whole grouping algorithm is linear in the total size of the rules to be grouped. Note also that, besides the computational space needed to store the ....
R. Srikant. Fast Algorithms for Mining Association Rules and Sequential Pat- terns. PhD thesis, University of Wisconsin, Madison, 1996.
Expert-Driven Validation of Rule-Based User Models in .. - Gediminas.. (Correct)
....set. As the result, the running time of a rule aggregation (lines 5 6) is linear in the size of the rule (i.e. total number of attributes in the body and the head of the rule) Also, since the group set GroupSet is implemented as a hash tree data structure (similar to the one described by [Sri96] an insertion of a group into the resulting group set G (line 7) is also linear in the size of the rule. Consequently, the running time of the whole grouping algorithm is linear in the total size of the rules to be grouped. Note also that, besides the computational space needed to store the ....
R. Srikant. Fast Algorithms for Mining Association Rules and Sequential Patterns. PhD thesis, University of Wisconsin, Madison, 1996.
Fast Algorithms for Discovering the Maximum Frequent Set - Lin (1998) (Correct)
.... survey papers regarding data mining problems can be found in, e.g. FPS96] FPSU96] M97] PBKKS97] In addition to the algorithms discussed so far, there has been extensive research relating to the problem of association rule mining such as [BMS97] GKMT97] HCC92] HF95] MT96b] ORS98] [S96], SA95b] SA96b] SVA97] T96a] and [KMRTB94] Similar candidate pruning techniques has been applied to discover sequential patterns (e.g. MT95] MT96c] SA95a] SA96a] Z97] and episodes (e.g. MT95] MT96c] Some other papers concentrate on designing parallel algorithms on ....
R. Srikant. Fast algorithm for mining association rules and sequential patterns. Ph.D. Thesis, University of Wisconsin, Madison, 1996.
Data Mining Approaches for Intrusion Detection - Lee, Stolfo (1998) (65 citations) (Correct)
....specific permissions, is very consistent. These consistent behaviors can be captured in association rules; ffl We can continuously merge the rules from a new run to the aggregate rule set (of all previous runs) Our implementation follows the general association rules algorithm, as described in [Sri96] 3.2 Frequent Episodes While the association rules algorithm seeks to find intraaudit record patterns, the frequent episodes algorithm, as described in [MTV95] can be used to discover inter audit record patterns. A frequent episode is a set of events that occur frequently within a time window ....
R. Srikant. Fast Algorithms for Mining Association Rules and Sequential Patterns. PhD thesis, University of Wisconsin - Madison, 1996.
Survey on Frequent Pattern Mining - Goethals (2003) (4 citations) (Correct)
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R. Srikant. Fast algorithms for mining association rules and sequential patterns. PhD thesis, University of Wisconsin, Madison, 1996.
Efficient Frequent Pattern Mining - Goethals (2002) (Correct)
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R. Srikant. Fast algorithms for mining association rules and sequential patterns. PhD thesis, University of Wisconsin, Madison, 1996.
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