Analisis Consumer Behaviour Pada Toko Retail Dengan Metode APRIORI-SD

Nathaniel Edward(1*), Rolly Intan(2), Alvin Nathaniel Tjondrowiguno(3),


(1) Program Studi Informatika
(2) Program Studi Informatika
(3) Program Studi Informatika
(*) Corresponding Author

Abstract


Retail store needs to evolve especially in digital age where ecommerce
becoming more and more common and most people
prefer the convenience of an e-commerce. One of the biggest
advantage of a “newer” e-commerce is they build they’re
business model on the foundation of processing data, whereas
older retail store doesn’t. Development of data mining and
machine learning are pushing older business model to do better.
This journal represents the possibilities of using subgroup
discovery as a method of analyzing transactional data. Subgroup
discovery is a data mining technique which extracts interesting
rule. APRIORI-SD is a method within subgroup discovery where
evaluation measure use by APRIORI-SD already prioritizing
unusualness distribution of a given data.
The result of this knowledge are able to find anomalies such as
differentiating subgroup(s) with differences up to 50% compared
to overall distribution percentage. With the result people are
able to create a better strategies in the future.


Keywords


APRIORI, APRIORI-SD, Subgroup discovery

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References


Herrera, Franciso., Gonzalez, Pedo. 2010. An Overview on

Subgroup discovery: Foundations and Applications. London

Jovanoski, Viktor., Lavrac, Nada. 2002 Classification Rule

Learning with APRIORI-C. Slovenia

Kaur, Manpreet., Kang, Shivani. 2016. Market Basket

Analysis: Identify the changing trends of market data using

association rule mining. India

Kavsek, Branko, Lavrac, Nada. 2006. Adapting Association

Rule Learning to Subgroup discovery

Kumar, Dr.V. Srinivasa Kumar., Dr. R. Renganathan. 2018.

Consumer Buying Pattern analysis using Apriori Association

Rule.

Liu, bing. Hsu, Wynne 1998. Integrating Classification and

Association Rule Mining. National University of Singapore.

Mining Interest In Online Shoppers’ Data: An Association

Rule Mining Approach. 2018. Acta Polytechnica Hungarica,

(7). doi:10.12700/aph.14.7.2017.7.9

Sani, M.Fani., van der Aalst, W.M.P. 2017. Subgroup

Discovery in Process Mining. Eindhoven University of

Technology.

Samli, A. C. 2015. Consumer Behavior and Retail Strategy.

Coping with Retail Giants, 83-102.

doi:10.1057/9781137476340_8

Todorovski, Ljupco. Lavrac, Nada. 2000 Predictive

Performance of Weighted Relative Accuracy. University of

Bristol.


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