Franklin

Data Mining in Large Sets of Complex Data [electronic resource] / by Robson Leonardo Ferreira Cordeiro, Christos Faloutsos, Caetano Traina Júnior.

Author/Creator:
Ferreira Cordeiro, Robson Leonardo. author., Author,
Publication:
London : Springer London : Imprint: Springer, 2013.
Format/Description:
Book
1 online resource (XI, 116 pages) : 37 illustrations, 25 illustrations in color.
Edition:
1st ed. 2013.
Series:
Computer Science (Springer-11645)
SpringerBriefs in computer science 2191-5768
SpringerBriefs in Computer Science, 2191-5768
Contained In:
Springer eBooks
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Subjects:
Data mining.
Database management.
Local subjects:
Data Mining and Knowledge Discovery. (search)
Database Management. (search)
System Details:
text file PDF
Summary:
The amount and the complexity of the data gathered by current enterprises are increasing at an exponential rate. Consequently, the analysis of Big Data is nowadays a central challenge in Computer Science, especially for complex data. For example, given a satellite image database containing tens of Terabytes, how can we find regions aiming at identifying native rainforests, deforestation or reforestation? Can it be made automatically? Based on the work discussed in this book, the answers to both questions are a sound "yes", and the results can be obtained in just minutes. In fact, results that used to require days or weeks of hard work from human specialists can now be obtained in minutes with high precision. Data Mining in Large Sets of Complex Data discusses new algorithms that take steps forward from traditional data mining (especially for clustering) by considering large, complex datasets. Usually, other works focus in one aspect, either data size or complexity. This work considers both: it enables mining complex data from high impact applications, such as breast cancer diagnosis, region classification in satellite images, assistance to climate change forecast, recommendation systems for the Web and social networks; the data are large in the Terabyte-scale, not in Giga as usual; and very accurate results are found in just minutes. Thus, it provides a crucial and well timed contribution for allowing the creation of real time applications that deal with Big Data of high complexity in which mining on the fly can make an immeasurable difference, such as supporting cancer diagnosis or detecting deforestation.
Contents:
Preface
Introduction
Related Work and Concepts
Clustering Methods for Moderate-to-High Dimensionality Data
Halite
BoW
QMAS
Conclusion.
Contributor:
Faloutsos, Christos. author., Author,
Traina Júnior, Caetano. author., Author,
SpringerLink (Online service)
Other format:
Printed edition:
Printed edition:
ISBN:
978-1-4471-4890-6
9781447148906
Publisher Number:
10.1007/978-1-4471-4890-6 doi
Access Restriction:
Restricted for use by site license.