Mining Sequential Rules By Applying Sliding Window Constraint / Najlacnejšie knihy
Mining Sequential Rules By Applying Sliding Window Constraint

Kód: 19747475

Mining Sequential Rules By Applying Sliding Window Constraint

Autor Sandipkumar Sagare

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 mul ... celý popis

32.06

Bežne: 37.66 €

Ušetríte 5.60 €


Skladom u dodávateľa
Odosielame za 8 - 11 dní
Pridať medzi želanie

Mohlo by sa vám tiež páčiť

Darčekový poukaz: Radosť zaručená
  1. Darujte poukaz v ľubovoľnej hodnote, a my sa postaráme o zvyšok.
  2. Poukaz sa vzťahuje na všetky produkty v našej ponuke.
  3. Elektronický poukaz si vytlačíte z e-mailu a môžete ho ihneď darovať.
  4. Platnosť poukazu je 12 mesiacov od dátumu vystavenia.

Objednať darčekový poukazViac informácií

Viac informácií o knihe Mining Sequential Rules By Applying Sliding Window Constraint

Nákupom získate 78 bodov

Anotácia knihy

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.

Parametre knihy

32.06

Obľúbené z iného súdka



Osobný odber Bratislava a 12744 dalších

Copyright ©2008-26 najlacnejsie-knihy.sk Všetky práva vyhradenéSúkromieCookies


Môj účet: Prihlásiť sa
Všetky knihy sveta na jednom mieste. Navyše za skvelé ceny.

Nákupný košík ( prázdny )

Vyzdvihnutie v Zásielkovni
zadarmo nad 59,99 €.

Nachádzate sa: