Data Mining in Large Sets of Complex Data

1. Front Matter

   Pages i-xi

   PDF

2. Introduction

   • Robson L. F. Cordeiro, Christos Faloutsos, Caetano Traina Júnior

   Pages 1-6

3. Related Work and Concepts

   • Robson L. F. Cordeiro, Christos Faloutsos, Caetano Traina Júnior

   Pages 7-20

4. Clustering Methods for Moderate-to-High Dimensionality Data

   • Robson L. F. Cordeiro, Christos Faloutsos, Caetano Traina Júnior

   Pages 21-32

5. Halite

   • Robson L. F. Cordeiro, Christos Faloutsos, Caetano Traina Júnior

   Pages 33-67

6. BoW

   • Robson L. F. Cordeiro, Christos Faloutsos, Caetano Traina Júnior

   Pages 69-92

7. QMAS

   • Robson L. F. Cordeiro, Christos Faloutsos, Caetano Traina Júnior

   Pages 93-109

8. Conclusion

   • Robson L. F. Cordeiro, Christos Faloutsos, Caetano Traina Júnior

   Pages 111-116

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.

• Analysis of Breast Cancer Data
• Analysis of Large Graphs from Social Networks
• Analysis of Satellite Imagery
• Big Data
• Correlation Clustering
• Data Analysis with MapReduce
• Data Mining
• Linear or Quasi-linear Complexity
• Low-labor Labeling
• Summarization and Attention Routing

From the reviews:

“This book is a must-read for all data mining professionals, as it explains new and superior techniques for clustering large datasets of high-dimensional data. It would also be interesting for professionals who work with large volumes of complex data and want real-time information for better decision making.” (Alexis Leon, Computing Reviews, July, 2013)

• Computer Science Department - ICMC, University of São Paulo, São Carlos, Brazil

  Robson L. F. Cordeiro, Caetano Traina Júnior

• School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

  Christos Faloutsos

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