customers are the most valuable asset of an organization. due to high contest in the business field, it is necessary to regard the customer relationship management (crm) of the enterprise. data mining and machine learning methods been utilized by businesses in recent years in order to improve crm. crm is the strategy for building, managing, and strengthening loyal and long lasting customer rela...

advanced data mining techniques can be used in universities classification, discovering specific patterns in the determination of successful students, design of a plan or a teaching method and finding critical points of financial management. in this article, we proposed a method to predict the rate of student enrollment in coming years. the data for this research were from data sets of voluntee...

This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...

This paper presents a data mining methodology to analyze the careers of University graduated students. We present different approaches based on clustering and sequential patterns techniques in order to identify strategies for improving the performance of students and the scheduling of exams. We introduce an ideal career as the career of an ideal student which has taken each examination just aft...

ion of GeoDatabases Geographic Database Conceptual Modeling Modeling with a UML Profile Geographic Databases Spatio-temporal Database Modeling with an Extended Entity-Relationship Model Geographic Dynamics, Visualization and Modeling MAY YUAN Department of Geography and Center for Spatial Analysis, University of Oklahoma, Norman, OK, USA

This paper extracts and documents patterns that identify recurring solutions for the problem of evaluation of data mining models. The five patterns presented in this paper are organized as a pattern language. The patterns differ in their context of application and how they solve the evaluation problem, especially when only limited amounts of data are available. Another contribution of this pape...