Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data:Basic knowledge (data & challenges) on social media analyticsClustering as a fundamental technique for unsupervised knowledge discovery and data miningA class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domainAdaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction.It presents initiatives on the mathematical demonstration of ART’s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks.Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you:How to process big streams of multimedia data?How to analyze social networks with heterogeneous data?How to understand a user’s interests by learning from online posts and behaviors?How to create a personalized search engine by automatically indexing and searching multimodal information resources?           .

Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data:

Basic knowledge (data & challenges) on social media analyticsClustering as a fundamental technique for unsupervised knowledge discovery and data miningA class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domain

Adaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction.

It presents initiatives on the mathematical demonstration of ART’s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks.

Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you:

How to process big streams of multimedia data?How to analyze social networks with heterogeneous data?How to understand a user’s interests by learning from online posts and behaviors?How to create a personalized search engine by automatically indexing and searching multimodal information resources?          

.

This book constitutes the refereed proceedings of the 4th International Joint Conference an Ambient Intelligence, AmI 2013, held in Dublin, Ireland, in December 2013. The 15 revised full papers, 4 papers from the landscape track, 3 papers from the doctoral colloquium and 6 demo and poster papers were carefully reviewed and selected from numerous submissions and are presented with 6 workshop descriptions. The papers cover a variety of multi-disciplinary topics in computer science, human computer interaction, electrical engineering, industrial design, behavioral sciences, distributed devices, ubiquitous and communication technologies, pervasive computing, intelligent user interfaces and artificial intelligence.

This book constitutes the refereed proceedings of the 4th International Joint Conference an Ambient Intelligence, AmI 2013, held in Dublin, Ireland, in December 2013. The 15 revised full papers, 4 papers from the landscape track, 3 papers from the doctoral colloquium and 6 demo and poster papers were carefully reviewed and selected from numerous submissions and are presented with 6 workshop descriptions. The papers cover a variety of multi-disciplinary topics in computer science, human computer interaction, electrical engineering, industrial design, behavioral sciences, distributed devices, ubiquitous and communication technologies, pervasive computing, intelligent user interfaces and artificial intelligence.

This book constitutes the proceedings of the International Conference on Brain Informatics and Health, BIH 2014, held in Warsaw, Poland, in August 2014, as part of 2014 Web Intelligence Congress, WIC 2014. The 29 full papers presented together with 23 special session papers were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on brain understanding; cognitive modelling; brain data analytics; health data analytics; brain informatics and data management; semantic aspects of biomedical analytics; healthcare technologies and systems; analysis of complex medical data; understanding of information processing in brain; neuroimaging data processing strategies; advanced methods of interactive data mining for personalized medicine.

This book constitutes the proceedings of the International Conference on Brain Informatics and Health, BIH 2014, held in Warsaw, Poland, in August 2014, as part of 2014 Web Intelligence Congress, WIC 2014. The 29 full papers presented together with 23 special session papers were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on brain understanding; cognitive modelling; brain data analytics; health data analytics; brain informatics and data management; semantic aspects of biomedical analytics; healthcare technologies and systems; analysis of complex medical data; understanding of information processing in brain; neuroimaging data processing strategies; advanced methods of interactive data mining for personalized medicine.

This book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 14 revised full papers presented together with one keynote talk were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections

This volume contains the proceedings of UIC 2009, the 6th International C- ference on Ubiquitous Intelligence and Computing: Building Smart Worlds in Real and Cyber Spaces. The UIC 2009 conference was technically co-sponsored by the IEEE and the IEEE Computer Society Technical Committee on Scalable Computing. The conference was also sponsored by the Australian Centre of - cellence in Information and Communication Technologies (NICTA). UIC 2009 was accompanied by six workshops on a variety of research challenges within the area of ubiquitous intelligence and computing. The conference was held in Brisbane, Australia, July 7–9, 2009. The event was the sixth meeting of this conference series. USW 2005 (First International Workshop on Ubiquitous Smart World), held in March 2005 in Taiwan, was the ?rst event in the series. This event was followed by UISW 2005 (Second International Symposium on Ubiquitous Intelligence and Smart Worlds) held in December 2005 in Japan. Since 2006, the conference has been held annually under the name UIC (International Conference on Ubiquitous Intelligence and Computing). UIC 2006 was held in September 2006 in Wuhan andThreeGorges,China,followedbyUIC2007heldinJuly2007inHongKong, and UIC 2008 held in June 2008 in Oslo, Norway. Ubiquitous sensors,computers, networksand informationare paving the way towardasmartworldinwhichcomputationalintelligenceisdistributedthrou- out the physical environment to provide reliable and relevant services to people.

Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data:Basic knowledge (data & challenges) on social media analyticsClustering as a fundamental technique for unsupervised knowledge discovery and data miningA class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domainAdaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction.It presents initiatives on the mathematical demonstration of ART’s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks.Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you:How to process big streams of multimedia data?How to analyze social networks with heterogeneous data?How to understand a user’s interests by learning from online posts and behaviors?How to create a personalized search engine by automatically indexing and searching multimodal information resources?           .

Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data:

Basic knowledge (data & challenges) on social media analyticsClustering as a fundamental technique for unsupervised knowledge discovery and data miningA class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domain

Adaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction.

It presents initiatives on the mathematical demonstration of ART’s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks.

Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you:

How to process big streams of multimedia data?How to analyze social networks with heterogeneous data?How to understand a user’s interests by learning from online posts and behaviors?How to create a personalized search engine by automatically indexing and searching multimodal information resources?          

.

This book constitutes the refereed proceedings of the 4th International Joint Conference an Ambient Intelligence, AmI 2013, held in Dublin, Ireland, in December 2013. The 15 revised full papers, 4 papers from the landscape track, 3 papers from the doctoral colloquium and 6 demo and poster papers were carefully reviewed and selected from numerous submissions and are presented with 6 workshop descriptions. The papers cover a variety of multi-disciplinary topics in computer science, human computer interaction, electrical engineering, industrial design, behavioral sciences, distributed devices, ubiquitous and communication technologies, pervasive computing, intelligent user interfaces and artificial intelligence.

This book constitutes the refereed proceedings of the 4th International Joint Conference an Ambient Intelligence, AmI 2013, held in Dublin, Ireland, in December 2013. The 15 revised full papers, 4 papers from the landscape track, 3 papers from the doctoral colloquium and 6 demo and poster papers were carefully reviewed and selected from numerous submissions and are presented with 6 workshop descriptions. The papers cover a variety of multi-disciplinary topics in computer science, human computer interaction, electrical engineering, industrial design, behavioral sciences, distributed devices, ubiquitous and communication technologies, pervasive computing, intelligent user interfaces and artificial intelligence.

This book constitutes the proceedings of the International Conference on Brain Informatics and Health, BIH 2014, held in Warsaw, Poland, in August 2014, as part of 2014 Web Intelligence Congress, WIC 2014. The 29 full papers presented together with 23 special session papers were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on brain understanding; cognitive modelling; brain data analytics; health data analytics; brain informatics and data management; semantic aspects of biomedical analytics; healthcare technologies and systems; analysis of complex medical data; understanding of information processing in brain; neuroimaging data processing strategies; advanced methods of interactive data mining for personalized medicine.

This book constitutes the proceedings of the International Conference on Brain Informatics and Health, BIH 2014, held in Warsaw, Poland, in August 2014, as part of 2014 Web Intelligence Congress, WIC 2014. The 29 full papers presented together with 23 special session papers were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on brain understanding; cognitive modelling; brain data analytics; health data analytics; brain informatics and data management; semantic aspects of biomedical analytics; healthcare technologies and systems; analysis of complex medical data; understanding of information processing in brain; neuroimaging data processing strategies; advanced methods of interactive data mining for personalized medicine.

This book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 14 revised full papers presented together with one keynote talk were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections

This volume contains the proceedings of UIC 2009, the 6th International C- ference on Ubiquitous Intelligence and Computing: Building Smart Worlds in Real and Cyber Spaces. The UIC 2009 conference was technically co-sponsored by the IEEE and the IEEE Computer Society Technical Committee on Scalable Computing. The conference was also sponsored by the Australian Centre of - cellence in Information and Communication Technologies (NICTA). UIC 2009 was accompanied by six workshops on a variety of research challenges within the area of ubiquitous intelligence and computing. The conference was held in Brisbane, Australia, July 7–9, 2009. The event was the sixth meeting of this conference series. USW 2005 (First International Workshop on Ubiquitous Smart World), held in March 2005 in Taiwan, was the ?rst event in the series. This event was followed by UISW 2005 (Second International Symposium on Ubiquitous Intelligence and Smart Worlds) held in December 2005 in Japan. Since 2006, the conference has been held annually under the name UIC (International Conference on Ubiquitous Intelligence and Computing). UIC 2006 was held in September 2006 in Wuhan andThreeGorges,China,followedbyUIC2007heldinJuly2007inHongKong, and UIC 2008 held in June 2008 in Oslo, Norway. Ubiquitous sensors,computers, networksand informationare paving the way towardasmartworldinwhichcomputationalintelligenceisdistributedthrou- out the physical environment to provide reliable and relevant services to people.