presentasi data mining (2)
DESCRIPTION
presentasi untuk ujianTRANSCRIPT
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Creating Tour Packages in
Bandung Based onMarket Basket Analysis
Ika Pretty S Kega Kurniawan Mahendra Sunt S Ruth Teodora
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+
=Knowledge
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Knowledge Discovery Proc
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Data Mining Techniques
ClusteringClassification Association
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Data
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Our Analysis Method
Apriori
FP-Growth algorithm
Association Rules Method (Market-BasketAnalysis)
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Apriori
Apriori is an algorithm for frequent item set mining and associ
rule learning over transactional databases.It proceeds by identifying the frequent individual items in the
database and extending them to larger and larger item sets as
as those item sets appear sufficiently often in the database.
The frequent item sets determined by Apriori can be used to
determine association rules which highlight general trends in t
database: this has applications in domains such as market ba
analysis.
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Frequent Pattern Analysis
This stage looking for a combination of items that meet the
minimum requirements of the support values in the database.
Support the value of an item is obtained by the following form
The support from 2 items obtained from the following formu
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Association Rule
After all of the high frequency pattern is found, then look for
association rules which meet the minimum requirements for
calculating confidence with associative rule A => B. Confiden
value of the rule A => B is obtained by the following formula
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Our Target
Determining tour
packages
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Our Software
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Rapidminer Node Schema
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Result
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Travel
Pack
Confi0.
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Travel
Pack
Confi0.
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Travel
Pack
Confi0.
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Travel
Pack
Confi0.
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Travel
Pack
Confi0.
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Travel
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Confi0.
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Conclusion
Its easy using RapidMiner as a software to analyze the dano coding required to processing the data.
Apriori method in Association rule presentsthe value of cthat can be used to determine a tour packages.
Based on the results of RapidMiner, the favorite package package with a confidence value of 0.783.
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