Kniha Mining Sequential Rules By Applying Sliding Window Constraint Sandipkumar Sagare

Mining Sequential Rules By Applying Sliding Window Constraint

Mining of Partially-Ordered Sequential Rules (POSR) By Applying the Sliding-Window Constraint

Jazyk: Angličtina
Vazba: Brožovaná
Dostupnost: Skladem u dodavatele
Odesíláme za 8-11 dnů
710
Sequential rule mining is used to extract important data in various application such as stock market...

Informace o knize

Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2018
Stránek
64
EAN
9786139841417
Enbook ID
19747475
Hmotnost
104
Rozměry
152 x 229 x 4

Kompletní popis

Sequential rule mining is used to extract important data in various application such as stock market analysis, e-commerce. It generally includes identifying sequential rules from given sequence database which will be common in multiple sequences. Partially Ordered Sequential rules (POSR) is a type of sequential rules in which the items in left and right side of the sequential rule does not need to be ordered. The existing approaches used for mining POSR include RuleGrowth Algorithm, TRuleGrowth Algorithm. But, these approaches either not use sliding window size constraint (RuleGrowth) or take more execution time even after using the sliding window size constraint (TRuleGrowth).This book presents a technique called M_TRuleGrowth which takes the sequence database as input and applies minimum support, minimum confidence, and window size constraints respectively to generate partially-ordered sequential rules. The experimental evaluation in terms of number of rules generated and execution time is conducted to compare the technique with existing approaches. It is found that the M_TRuleGrowth performs better in terms of execution time.

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