a study on redescriptive mining techniques on various domains

Abstract

Over-riding the Statistical database systems, which supports only the socio-economic data, Data Warehouse systems on the other hand, serve users or knowledge workers in the role of data analysis and decision making have come in to existence. Such systems can organize and present data in various formats in order to accommodate the diverse needs of different users. newline Data mining, the field of extraction in business analytics and commercial data processing disciplines utilizes that kind of Data warehouse systems. Data mining involves an integration of techniques from multiple disciplines such as database technology, statistics, machine learning, high-performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis, D.Kumar[2007]. Now the increased computational capabilities have accelerated and accumulated large amounts of scientific data and have consequently invited Data mining in scientific domains. The major data mining tasks like anomaly detection, Association rule learning, classification, clustering, and regression have been applied in to important scientific domains include Bioinformatics, geology, genetics, power transformers, educational research, sensors, cosmology, chemical and material engineering, and geophysics. Business domain data is more homogenous and comparatively lesser than the scientific data. As a result, although most of the business-oriented data mining algorithms work for scientific datasets, special methodologies are required to work with scientific data more effectively and efficiently. newline