A CLUSTER ANALYSIS AND DECISION TREE HYBRID APPROACH IN DATA MINING TO DESCRIBING TAX AUDIT

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

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Field Value
 
Title A CLUSTER ANALYSIS AND DECISION TREE HYBRID APPROACH IN DATA MINING TO DESCRIBING TAX AUDIT
 
Creator Dhiman, Richa
Vashisht, Sheveta
Sharma, Kapil
 
Subject Computer Science; Information Technology
Clustering; Decision tree; HAC; SOM; C4.5.
 
Description In this research, we use clustering and classification methods to mine the data of tax and extract the information about tax audit by using hybrid algorithms K-MEANS, SOM and HAC algorithms from clustering and CHAID and C4.5 algorithms from decision tree and it produce the better results than the traditional algorithms and compare it by applying on tax dataset. Clustering method will use for make the clusters of similar groups to extract the easily features or properties and decision tree method will use for choose to decide the optimal decision to extract the valuable information from samples of tax datasets? This comparison is able to find clusters in large high dimensional spaces efficiently. It is suitable for clustering in the full dimensional space as well as in subspaces. Experiments on both synthetic data and real-life data show that the technique is effective and also scales well for large high dimensional datasets
 
Publisher CIRWORLD
 
Date 2005-06-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://cirworld.com/index.php/ijct/article/view/3111
 
Source INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY; Vol 4 No 1c; 114-119
2277-3061
 
Language eng
 
Relation http://cirworld.com/index.php/ijct/article/view/3111/3024
 
Rights Copyright (c) 2016 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
http://creativecommons.org/licenses/by-nc-nd/4.0
 

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