Purpose: this study wants to find any relationship between the numbers of purchase and the income the customer brings to the company. The attempt is to find those customers who buy more than one life insurance policy and represent the signs of good payments at the same time by the help of data mining tools. Design/ methodology/ approach: the approach of this research is to use data mining tools...

Data Mining (DM) is a very crucial issue in knowledge discovery processes. The basic facilities to create data mining models were implemented successfully on Oracle 9i as the extension of the database server. DM tools enable developers to create Business Intelligence (BI) applications. As a result Data Mining models can be used as support of knowledge-based management. The main goal of the pape...

With the diffusion of digital information technology, data mining (DM) is widely expected to increase the productivity of all kinds of research activities. Based on bibliometric data, we demonstrate that the share of DM-related research articles in all published academic papers has increased substantially over the last two decades. We develop an ordinal categorization of countries according to ...

We present a tool, PDM (Planning for Data Mining), based on Automated Planning that helps users (non necessarily experts on data mining) to perform DM (Data Mining) tasks. The starting point is a definition of the DM task to be carried out and the output is a set of plans that are executed in a DM tool to obtain a set of models and statistics. Plans are data-mining knowledge flows, i.e. sequenc...

Operator selection is the task of selecting the right operator for building not only valid but also optimal data mining (DM) workflows in order to solve a new learning problem. One of the main achievements of the EU-FP7 e-LICO project has been to develop an Intelligent Data-Mining Assistant (IDA) to assist the DM user in the construction of such DM workflows following a cooperative AI-planning ...

The rise of data mining has brought many changes to people’s lives but also to companies and the importance of data analysis. Companies always had a tendency to gather as much data as possible but it has only been recently due to the developments in IT that large quantities of data can be analyzed in a fast and easy way. This new field gave rise to the methodology of Cross Industry Standard Pro...

Data Mining (DM) is the science of extracting useful and non-trivial information from the huge amounts of data that is possible to collect in many and diverse fields of science, business and engineering. Due to its relatively recent development, Data Mining still poses many challenges to the research community. New methodologies are needed in order to mine more interesting and specific informat...

classifying customers using data mining algorithms, enables banks to keep old customers loyality while attracting new ones. using decision tree as a data mining technique, we can optimize customer classification provided that the appropriate decision tree is selected. in this article we have presented an appropriate model to classify customers who use internet banking service. the model is deve...

When we talk about using neural networks for data mining we have in mind the original data mining scope and challenge. How did neural networks meet this challenge? Can we run neural networks on a dataset with gigabytes of data and millions of records? Can we provide explanations of discovered patterns? How useful that patterns are? How to distinguish useful, interesting patterns automatically? ...

Very often data relevant to one search is not located at a single site, it may be widely-distributed and in many different forms. Similarly there may be a number of algorithms that may be applied to a single Knowledge Discovery in Databases (KDD) task with no obvious “best” algorithm. There is a clear advantage to be gained from a software organisation that can locate, evaluate, consolidate and...