Linear Principal Components Analysis (LPCA) is known for its simplicity to reduce the features dimensionality. An extension of LPCA, Kernel Principal Components Analysis (KPCA), outperforms LPCA when applied on non-linear data in high dimensional feature space. However, on large datasets with high input space, KPCA deals with a memory issue and imbalance classification problems with difficulty....

CRM gains increasing importance due to intensive competition and saturated markets. With the purpose of retaining customers, academics as well as practitioners find it crucial to build a churn prediction model that is as accurate as possible. This study applies support vector machines in a newspaper subscription context in order to construct a churn model with a higher predictive performance. M...

Presently, customer retention is essential for reducing churn in telecommunication industry. Customer prediction (CCP) important to predict the possibility of quality services. Since risks also get essential, rise machine learning (ML) models can be employed investigate characteristics behavior. Besides, deep (DL) help behavior based characteristic data. DL necessitate hyperparameter modelling ...

Customer churn prediction is very important for e-commerce enterprises to formulate effective customer retention measures and implement successful marketing strategies. According the characteristics of longitudinal timelines multidimensional data variables B2C customers’ shopping behaviors, this paper proposes a loss model based on combination k-means segmentation support vector machine (SVM) p...

Increasing network intrusion becoming crucial problem in security infrastructures. Data mining techniques have been successfully applied in many different fields including insolvency prediction, churn prediction, marketing, process control, fraud detection, and network management. Today number of research projects is using data mining for intrusion detection system (IDS) and prevention. The goa...

This paper summarises a successful application of Knowledge Discovery in Databases (KDD) in an Italian telecommunications research lab. The aim of the application was to predict customer churn behaviour. A critical success factor for this application was clever preprocessing of the given data, in particular the construction of derived predictor features. The application was realised in the Mini...