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Foundations of Computational Intelligence Volume 4: Bio-Inspired
Data Mining Theoretical Foundations and Applications Recent
advances in the computing and electronics technology, particularly
in sensor devices, databases and distributed systems, are leading
to an exponential growth in the amount of data stored in databases.
It has been estimated that this amount doubles every 20 years. For
some applications, this increase is even steeper. Databases storing
DNA sequence, for example, are doubling their size every 10 months.
This growth is occurring in several applications areas besides
bioinformatics, like financial transactions, government data,
environmental mo- toring, satellite and medical images, security
data and web. As large organizations recognize the high value of
data stored in their databases and the importance of their data
collection to support decision-making, there is a clear demand for
- phisticated Data Mining tools. Data mining tools play a key role
in the extraction of useful knowledge from databases. They can be
used either to confirm a parti- lar hypothesis or to automatically
find patterns. In the second case, which is - lated to this book,
the goal may be either to describe the main patterns present in
dataset, what is known as descriptive Data Mining or to find
patterns able to p- dict behaviour of specific attributes or
features, known as predictive Data Mining. While the first goal is
associated with tasks like clustering, summarization and
association, the second is found in classification and regression
problems.
Foundations of Computational Intelligence Volume 6: Data Mining:
Theoretical Foundations and Applications Finding information hidden
in data is as theoretically difficult as it is practically
important. With the objective of discovering unknown patterns from
data, the methodologies of data mining were derived from
statistics, machine learning, and artificial intelligence, and are
being used successfully in application areas such as
bioinformatics, business, health care, banking, retail, and many
others. Advanced representation schemes and computational
intelligence techniques such as rough sets, neural networks;
decision trees; fuzzy logic; evolutionary algorithms; arti- cial
immune systems; swarm intelligence; reinforcement learning,
association rule mining, Web intelligence paradigms etc. have
proved valuable when they are - plied to Data Mining problems.
Computational tools or solutions based on intel- gent systems are
being used with great success in Data Mining applications. It is
also observed that strong scientific advances have been made when
issues from different research areas are integrated. This Volume
comprises of 15 chapters including an overview chapter providing an
up-to-date and state-of-the research on the applications of
Computational Int- ligence techniques for Data Mining. The book is
divided into 3 parts: Part-I: Data Click Streams and Temporal Data
Mining Part-II: Text and Rule Mining Part-III: Applications Part I
on Data Click Streams and Temporal Data Mining contains four
chapters that describe several approaches in Data Click Streams and
Temporal Data Mining.
Foundations of Computational Intelligence Volume 4: Bio-Inspired
Data Mining Theoretical Foundations and Applications Recent
advances in the computing and electronics technology, particularly
in sensor devices, databases and distributed systems, are leading
to an exponential growth in the amount of data stored in databases.
It has been estimated that this amount doubles every 20 years. For
some applications, this increase is even steeper. Databases storing
DNA sequence, for example, are doubling their size every 10 months.
This growth is occurring in several applications areas besides
bioinformatics, like financial transactions, government data,
environmental mo- toring, satellite and medical images, security
data and web. As large organizations recognize the high value of
data stored in their databases and the importance of their data
collection to support decision-making, there is a clear demand for
- phisticated Data Mining tools. Data mining tools play a key role
in the extraction of useful knowledge from databases. They can be
used either to confirm a parti- lar hypothesis or to automatically
find patterns. In the second case, which is - lated to this book,
the goal may be either to describe the main patterns present in
dataset, what is known as descriptive Data Mining or to find
patterns able to p- dict behaviour of specific attributes or
features, known as predictive Data Mining. While the first goal is
associated with tasks like clustering, summarization and
association, the second is found in classification and regression
problems.
Foundations of Computational Intelligence Volume 6: Data Mining:
Theoretical Foundations and Applications Finding information hidden
in data is as theoretically difficult as it is practically
important. With the objective of discovering unknown patterns from
data, the methodologies of data mining were derived from
statistics, machine learning, and artificial intelligence, and are
being used successfully in application areas such as
bioinformatics, business, health care, banking, retail, and many
others. Advanced representation schemes and computational
intelligence techniques such as rough sets, neural networks;
decision trees; fuzzy logic; evolutionary algorithms; arti- cial
immune systems; swarm intelligence; reinforcement learning,
association rule mining, Web intelligence paradigms etc. have
proved valuable when they are - plied to Data Mining problems.
Computational tools or solutions based on intel- gent systems are
being used with great success in Data Mining applications. It is
also observed that strong scientific advances have been made when
issues from different research areas are integrated. This Volume
comprises of 15 chapters including an overview chapter providing an
up-to-date and state-of-the research on the applications of
Computational Int- ligence techniques for Data Mining. The book is
divided into 3 parts: Part-I: Data Click Streams and Temporal Data
Mining Part-II: Text and Rule Mining Part-III: Applications Part I
on Data Click Streams and Temporal Data Mining contains four
chapters that describe several approaches in Data Click Streams and
Temporal Data Mining.
The International Symposium on Distributed Computing and Artificial
Intel- gence (DCAI10) is an annual forum that brings together past
experience, current work and promising future trends associated
with distributed computing, artificial intelligence and their
application to provide efficient solutions to real problems. This
symposium is organized by the Biomedicine, Intelligent System and
Edu- tional Technology Research Group (http: //bisite. usal. es/)
of the University of - lamanca. The present edition has been held
at the Polytechnic University of - lencia, from 7 to 10 September
2010, within the Congreso Espanol de Informatica (CEDI 2010).
Technology transfer in this field is still a challenge, with a
large gap between academic research and industrial products. This
edition of DCAI aims at contributing to reduce this gap, with a
stimulating and productive forum where these communities can work
towards future cooperation with social and econo- cal benefits.
This conference is the forum in which to present application of in-
vative techniques to complex problems. Artificial intelligence is
changing our - ciety. Its application in distributed environments,
such as internet, electronic commerce, environment monitoring,
mobile communications, wireless devices, distributed computing, to
cite some, is continuously increasing, becoming an e- ment of high
added value with social and economic potential, both industry, life
quality and research. These technologies are changing constantly as
a result of the large research and technical effort being
undertaken in universities, companies."
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