Data Analysis

This research is the first step in building an
efficient Decision Support System (DSS) which employs
Data Mining (DM) predictive, classification, clustering,
and association rules techniques. This step considers
finding groups of members in the dataset that are very
different from each other, and whose members are very
similar to each other, therefore one DM task is applied
which is clustering task. The main objective of the
proposed research is to enhance the performance of one
of the most well-known popular clustering algorithms
(K-mean) to produce near-optimal decisions for telcos
churn prediction and retention problems. Due to its
performance in clustering massive data sets. The final
clustering result of the k-mean clustering algorithm
greatly depends upon the correctness of the initial
centroids, which are selected randomly. This research
will be followed by a serious of researches targeting the
main objective which is an efficient DSS which will be
applied on customer banking data. In this research a
new method is proposed for finding the better initial
centroids to provide an efficient way of assigning the
data points to suitable clusters with reduced time
complexity. The proposed algorithm is successfully
developed an applied on customer banking data, and
the evaluation results are presented.