Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining
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TLDR
This chapter presents Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining and discusses how these techniques can be applied to time series data mining data.Abstract:
© 2012 Cassisi et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Miningread more
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Observing the Pulse of a City: A Smart City Framework for Real-Time Discovery, Federation, and Aggregation of Data Streams
Sefki Kolozali,Maria Bermudez-Edo,Nazli Farajidavar,Payam Barnaghi,Feng Gao,Muhammad Intizar Ali,Alessandra Mileo,Marten Fischer,Thorben Iggena,Daniel Kuemper,Ralf Tönjes +10 more
TL;DR: This work proposes a novel framework with an efficient semantic data processing pipeline, allowing for real-time observation of the pulse of a city and investigates the optimization of the semantic data discovery and integration based on the proposed stream quality analysis and data aggregation techniques.
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On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data
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Two level data aggregation protocol for prolonging lifetime of periodic sensor networks
TL;DR: A Two Level Data Aggregation (TLDA) Protocol for Prolonging the Lifetime of Periodic Sensor Networks is proposed and extensive simulation results are conducted using OMNeT++ network simulator and based on real data of sensor network to show the efficiency of the TLDA protocol compared with two existing methods.
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Multi-task learning for intelligent data processing in granular computing context
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TL;DR: This paper considers traditional machine learning to be single task learning, and argues the necessity to turn it into multi-task learning to allow an instance to belong to more than one class and to achieve class specific feature selection.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
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TL;DR: Good generalized these methods and gave elegant algorithms for which one class of applications is the calculation of Fourier series, applicable to certain problems in which one must multiply an N-vector by an N X N matrix which can be factored into m sparse matrices.