Support Interdisciplinary And Multidisciplinary Research Project

FOR THE GOVERNMENT:

1. Create programs which encourage the emergence of Data mining and knowledge discovery as an independent discipline.

2. Support interdisciplinary and multidisciplinary research projects. Many advances in Data mining require teams of mathematicians and statisticians, computer scientists, and application domain scientists working together to create the appropriate data sets and the required algorithms and software to analyze them.

3. Support basic research in computer and information sciences that underlies Data mining, including machine learning, knowledge systems, data bases, high performance computing, high performance networking, and digital libraries.

4. Support basic research in mathematics and statistics that underlies

This realization has come about as a result of the increasing loads of data being stored in organization’s databases. To take advantage of this storage data mining can use a data warehouse to manage the data before applying a data mining application. The reasons data mining has caught the attention of so many companies is that data mining has proven itself as a satisfactory tool. With the advent of ERP and ASP companies making progress in providing leading products and services, consolidation of data mining services alongside these services is a challenging path that can lead to very promising results. The future is still very uncertain. Because of the value that ERP and ASP companies can provide to organizations through their respective tools, an even greater benefit to companies is providing a data-mining tool that further analyzes the data. Data mining and knowledge discovery are emerging as a new discipline with important applications to science, engineering, health care, education, and business. Data mining is beginning to contribute research advances of its own, by providing scalable extensions and advances to work in associations, ensemble learning, graphical models, techniques for on-line discovery, and algorithms for the exploration of massive and distributed data sets. Advances in Data mining requires a) supporting single investigators working in Data mining and the underlying research domains supporting Data mining; b) supporting inter-disciplinary and multi-disciplinary research groups working on important basic and applied Data mining problems; and c) supporting the appropriate testbeds for mining large, massive and distributed data sets. Appropriate privacy and security models for Data mining must be developed and