The Role of Data Mining Technology in Electronic Transaction Expansion at Dashen Bank S.C
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Date
2011-07
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Addis Ababa University
Abstract
In this study the application of decision tree J48, ANN classification algorithms, and Kmeans
clustering algorithm of data mining on CRM the case of EFT of POS service of
the Dashen Bank S.C. have been discovered within the framework of CRISP-DM model.
The card holder customers data along with customer book information have been
collected, cleansed, integrated and transformed for testing using the clustering and
classification models. The final dataset consists of 11000 records in which different
clustering models at k values of 6, 5, and 4 with different seed values have been traced
and evaluated against their performances. The cluster model at k value of 6 with default
seed value has shown a better performance. Hence, the output of the best clustering
model (i.e. at k=6) has been used as an input for the decision tree and Artificial Neural
Network (ANN) classification models.
Different classifications with the J48 decision tree algorithm are tested with 10-fold cross
validation, and splitting the dataset into 70% for training and 30% for testing, techniques
by setting the cluster index formed by the cluster model as dependent variable and the
remaining variables as independent variables. Different decision tree classification
models with minNumObj =default, 5, 10, 15, 20, and 25 have been experimented. From
these decision tree parameters, a model with default parameter values showed the
maximum overall classification accuracy (i.e. 99.55%).
Likewise, different classification models of Multilayerperceptron ANN have been tested
by changing the hidden layer and learning rate parameter’s value. As a result, a model
with a classification accuracy of 99.97%, which is with default parameter value, was
chosen. Lastly, a comparison of the decision tree and ANN models in terms of the
overall classification accuracy , accuracy in classifying high level customers, and
accuracy in classifying low level/value customers have been undertaken. Therefore, the
ANN model has been the best in these evaluation parameters, and thus selected as a better
classifier in EFT of POS service customers.
The result obtained in this study was encouraging as it has very high classification
accuracy. This helps and strengthens the possible application of data mining to the
xi
banking industry in general, and in the EFT of POS service expansion marketing strategy
at the Dashen Bank S.C.
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Keywords
Data Mining