Comparison Of Apriori And Fp-Growth Algorithms On Determination Of Association Rules In Authorized Automobile Service Centres
AbstractData Mining is used to describe the totality of techniques which aim to find the unexplored patterns in a set of data. The purpose of data mining is to create models of decision-making devoted to estimations of future behavior based on analysis of past activities. In this study the shopping data of the customers of an authorized service, operating in the automative sector in Turkey, were analyzed using Apriori and FP-Growth Algorithms. This way, it is observed which products were purchased together by customers and in line with this observation, campaigns and promotions were given a direction to increase the profit.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Anadolu University in its journal Anadolu University Journal of Social Sciences.
Volume (Year): 12 (2012)
Issue (Month): 2 (June)
Contact details of provider:
Postal: Yunus Emre Kampusu 26470, Eskişehir
Phone: (90) (222) 335-0580 x 2743
Fax: (90) (222) 320-1304
Web page: http://www.anadolu.edu.tr/akademik/birim/genelBilgi/205/3429/1
More information through EDIRC
Data Mining; Association Rules; Apriori Algorithm; FP-Growth Algorithm; Market Basket Analysis;
Find related papers by JEL classification:
- C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Social Sciences Institute).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.