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ABSTRACT
Title |
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Frequent Item set Mining Using Global Profit Weight Algorithm |
Authors |
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ASHA RAJKUMAR, G.SOPHIA REENA |
Keywords |
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Global Profit Weight Algorithm;
Classification Algorithm; WEKA Tool |
Issue Date |
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November 2010 |
Abstract |
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The objective of the study focused on
weighted based frequent item set mining. The base paper
has proposed multi criteria based frequent item set for
weight calculation. Contribution towards this project is to
implement the global profit weight measure and test the
performance over utility based mining. For this project the
data consist of 90 products from automobile shop
including unit price, quantity sold and profit margin for
transaction set (one month data). Algorithm has been
implemented in Visual Basic for visualizing step by step
process calculations. Supervised machine learning
techniques namely Naïve Bayes Decision tree classifier,
VFI and IB1 Classifier are used for learning the model.
The results of the models are compared and observed that
Naïve Bayes performs well. WEKA tool is used to
classify the data set and accuracy is calculated.
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Page(s) |
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2519-2525 |
ISSN |
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0975–3397 |
Source |
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Vol. 2, Issue.8 |
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