The primary objective of NCDMW’12 is to establish an effective channel of communication between theoretical researchers and practitioners of Data Mining & Data Warehousing.. The theoretical research outcome provides necessary support to practitioners in Data Mining and Warehousing, and the difficulties faced by the practitioners in using the theoretical results provide feedback to the theoreticians to revalidate their models. NCDMW’12 thus meets the demand of both theoretical and applied researchers in Data Mining and Warehousing.
With the proliferation of data warehouses, data mining tools are flooding the market. Their objective is to discover hidden gold in your data. Many traditional report and query tools and statistical analysis systems use the term “data mining” in their product descriptions. Exotic Artificial Intelligence-based systems are also being touted as new data mining tools. The ultimate objective of data mining is knowledge discovery. Data mining methodology extracts hidden predictive information from large databases. With such a broad definition, however, an online analytical processing (OLAP) product or a statistical package could qualify as a data mining tool. Data mining methodology extracts hidden predictive information from large databases. That’s where technology comes in, for true knowledge discovery a data mining tool should unearth hidden information automatically.