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An enhancement of charm algorithm applied in market basket analysis / Angelica Joanna T. Correa and Carlo Gene G. De Lara. 6

By: Angelica Joanna T. Correa and Carlo Gene G. De Lara. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; March 2012.46Edition: Description: 28 cm. 104 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: ABSTRACT: This study focuses on the enhancement of charm algorithm applied in Market Basket Analysis. Market Basket analysis deals on suggesting products to customer base on their relationship among items. It could be derived base on the transactions where an item is included. The frequency of the item base on all the transaction in the database will be taken into consideration to determine how they are related to each other. Charm algorithm is a data mining algorithm which main function is to determine patterns and association among data. The researchers believe that the algorithm is best suited on the development of an application for market basket analysis and it can perform well even on large set of data in a database. The existing charm algorithm performs association of items better compare to other algorithms but the researchers recognize that the performance of the algorithm could be made better through the implementation of the objectives set for this study. The enhanced algorithm prevented the redundant scanning of database in gathering support by only updating the database. In addition, to reduce the time of association, the enhanced algorithm automatically output the item having 100% and will be disregarded in association. Lastly, the existing algorithm do not consider the quantity of an item when it is bought. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Archives	Filipiniana-Thesis	T QA76.9.C67.2012 (Browse shelf)	Available		FT6138

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Thesis: (BSCS major in Computer Science) - Pamantasan ng Lungsod ng Maynila. 2012. 56

ABSTRACT: This study focuses on the enhancement of charm algorithm applied in Market Basket Analysis. Market Basket analysis deals on suggesting products to customer base on their relationship among items. It could be derived base on the transactions where an item is included. The frequency of the item base on all the transaction in the database will be taken into consideration to determine how they are related to each other. Charm algorithm is a data mining algorithm which main function is to determine patterns and association among data. The researchers believe that the algorithm is best suited on the development of an application for market basket analysis and it can perform well even on large set of data in a database. The existing charm algorithm performs association of items better compare to other algorithms but the researchers recognize that the performance of the algorithm could be made better through the implementation of the objectives set for this study. The enhanced algorithm prevented the redundant scanning of database in gathering support by only updating the database. In addition, to reduce the time of association, the enhanced algorithm automatically output the item having 100% and will be disregarded in association. Lastly, the existing algorithm do not consider the quantity of an item when it is bought.

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