Information-Statistical Data Mining

As computer technology becomes more powerful, it becomes possible to collect data at a level, by size and the level of extent that could not even be imagined just a few years ago. At the same time, it also offers a growing possibility of discovering ... Read more

As computer technology becomes more powerful, it becomes possible to collect data at a level, by size and the level of extent that could not even be imagined just a few years ago. At the same time, it also offers a growing possibility of discovering intelligence from data through statistical techniques cornered as Exploratory Data Analysis (EDA). While EDA evolves to play a major role in the field of data mining, treatment for temporal spatial data remains a challenge. Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics will address this issue. This book will also attempt to address this issue through a framework that may allow us to answer at least partially, the following two important questions. First, how do we gain insights into understanding the intelligence behind the valuable information that data mining offers? More specifically, how do we interpret and evaluate the quality of information resulting from an EDA that is typically oriented around statistical techniques. Overall, Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics is written to introduce basic concepts, advanced research techniques, and practical solutions of data warehousing and data mining for hosting large data sets and EDA. This book is unique because it is one of the few in the forefront that attempts to bridge statistics and information theory through a concept of patterns. Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering. Less