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.