Name: Mining Sequential Rules By Applying Sliding Window Constraint (en Inglés)
SKU: 57298415
Price: 46.97 USD
Availability: InStock
Author: Sagare, Sandipkumar
ISBN: 9786139841417

portada Mining Sequential Rules By Applying Sliding Window Constraint (en Inglés)

Mining Sequential Rules By Applying Sliding Window Constraint (en Inglés)

Formato

Libro Físico

Autor

Sandipkumar Sagare

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x0.4 cm

Peso

0.10 kg.

ISBN13

9786139841417

Categorías

Teoría general del derecho

Mining Sequential Rules By Applying Sliding Window Constraint (en Inglés)

Sandipkumar Sagare (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

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Reseña del libro "Mining Sequential Rules By Applying Sliding Window Constraint (en Inglés)"

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.