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

Abstract

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