Adaptive Resonance Theory in Social Media Data Clustering: Roles, Methodologies, and Applications

200 Pages·2019·5.953 MB·English

by  Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II

Advanced Information and Knowledge Processing
Lei Meng
Ah-Hwee Tan
Donald C. Wunsch II
Adaptive Resonance
Theory in Social
Media Data
Clustering
Roles, Methodologies, and Applications
Advanced Information and Knowledge
Processing
Editors-in-Chief
Lakhmi C. Jain, Bournemouth University, Poole, UK, and, University of South
Australia, Adelaide, Australia
Xindong Wu, University of Vermont, USA
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Lei Meng Ah-Hwee Tan
(cid:129) (cid:129)
Donald C. Wunsch II
Adaptive Resonance Theory
in Social Media Data
Clustering
Roles, Methodologies, and Applications
123
LeiMeng Ah-Hwee Tan
NTU-UBCResearchCenterofExcellencein Schoolof Computer Science and
ActiveLiving forthe Elderly(LILY) Engineering
NanyangTechnological University NanyangTechnological University
Singapore, Singapore Singapore, Singapore
Donald C.WunschII
AppliedComputational Intelligence
Laboratory
MissouriUniversity ofScience and
Technology
Rolla, MO,USA
ISSN 1610-3947 ISSN 2197-8441 (electronic)
AdvancedInformation andKnowledge Processing
ISBN978-3-030-02984-5 ISBN978-3-030-02985-2 (eBook)
https://doi.org/10.1007/978-3-030-02985-2
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Preface
Scope
ComingintotheeraofWeb2.0,peopleareinvolvedinaconnectedandinteractive
Cyberworld, where the emergence of social networking websites has created
numerous interactive sharing and social network-enhanced platforms for users to
upload, comment, and share multimedia content online. It has led to a massive
number of web multimedia documents, together with their rich meta-information,
such as category information, user taggings and comments, and time-location
stamps. Such interconnected but heterogeneous social media data have provided
opportunities for better understanding traditional multimedia data, such as images
and text documents. More importantly, the different types of activities and inter-
actions of social users have pushed the bloom of artificial intelligence (AI) with
machine learning techniques, which shifts the typically data-centric research on
multimediaunderstandingtotheuser-centricresearch onsocialuserunderstanding
and numerous personalized services, such as user profiling, group-based social
behavior analysis, community and social trend discovery, and various social rec-
ommender systems based on users’ online behaviors, friendship networks, prefer-
ence inferences, etc. Additionally, recent advances in mobile devices tend to link
people with both the cyber and physical worlds, introducing a new topic of
online-offline analysis into the current form of social network analytics. All these
changes pose new questions and open challenges, and increase the needs for new
forms of machine learning techniques.
Clustering is an important approach to the analysis and mining of social media
datatofulfilltheaforementionedtasks.However,contrarytotraditionalmultimedia
data, information from the social media data is typically massive, diverse, hetero-
geneous,andnoisy.Thesecharacteristicsofsocialmediadataraisenewchallenges
for existing clustering techniques, including the scalability for big data, the ability
to automatically recognize data clusters, the strategies to effectively integrate data
v
vi Preface
fromheterogeneousresources,andtherobustnesstonoisyfeaturesandill-featured
patterns. Besides, online learning capability becomes a necessity in situations for
analyzing social media streams and capturing the evolving characteristics of social
networks and the underlying information. Moreover, social media data often has a
diverse range of topics while users typically have their own preferences for topics
hidden in the large amount of social media data, making incorporating user pref-
erences into the clustering process important to produce personalized results.
Thisbookisawareoftheopportunitiesandchallengesforclusteringalgorithms,
and is therefore aimed at systematically introducing frontiers in modern social
media analytics and presenting a class of clustering techniques based on adaptive
resonance theory (ART) for the fast and robust clustering of large-scale social
media data. With applications in a range of social media mining tasks, this book
demonstrates that these algorithms can handle one or more of the aforementioned
challenges with characteristics such as linear time complexity to scale up for big
data,onlinelearningcapability,automaticparameteradaptation,robustnesstonoisy
information, heterogeneous information fusion, and the ability to incorporate user
preferences.
Content
Thisbookhastwoparts:Theories(PartI)andApplications(PartII).PartIincludes
three chapters on background and algorithms, where
(cid:129) Chapter 1: introduces the characteristics of social media data, the roles and
challenges of clustering in social media analytics, and the authors’ approaches
based on the adaptive resonance theory (ART) to the aforementioned
challenges.
(cid:129) Chapter 2: offers a literature review on typical types of clustering algorithms
(potentially) applicable to social media analytics, and the key branches of
clustering-based social media mining tasks.
(cid:129) Chapter 3: is the cornerstone of this book, which proves the clustering mech-
anismofARTandillustratesaclassofclusteringalgorithmsbasedonARTthat
handlesthe characteristics ofdifferent types ofsocialmediadata for clustering.
In contrast, Part II provides real-world case studies on the major directions of
social media analytics using the ART-based solutions, where
(cid:129) Chapter 4: investigates clustering the surrounding text (title, description, com-
ments, etc.) of user-posted images for personalized web image organization.
(cid:129) Chapter 5: explores clustering composite Socially-enriched multimedia data, of
which each data item is (in part) described with different types of data, such as
images, surrounding text, and user comments.
Preface vii
(cid:129) Chapter6:presentsa studyon detecting user groups onsocialnetworks, where
the users with shared interests are discovered using their online posts and
behaviors, such as likes, sharing, and re-posting.
(cid:129) Chapter 7: depicts a clustering-based approach to indexing and retrieving
multimodal data in an online manner, with an application for building a mul-
timodal e-commerce product search engine.
(cid:129) Chapter 8: provides the conclusion for this book.
Audience
This book provides an up-to-date introduction on state-of-the-art clustering tech-
niques and the associated modern applications of social media analytics. It also
presents a class of clustering algorithms based on adaptive resonance theory
(ART) to address the challenges in social media data clustering.
The social web is growing in popularity and providing new forms of commu-
nicationofthesocialWeb,sothisbookisexpectedtoserveasastartingtutorialfor
researchers who are interested in clustering, ART, and social media mining, an
extensible research basis for further exploration, and a place to find practical
solutions to real-world applications on social media analytics.
This book will benefit readers from the following aspects:
1. Up-to-date Cutting-edge Research: This book summarizes state-of-the-art
innovative research on clustering and social media analytics in the 2010s,
published in top-tier and reputable conferences and journals across areas of
machine learning, data mining, and multimedia. The content of the book is
therefore valuable to fresh PhD students and researchers in the aforementioned
areas.
2. Fundamental Breakthrough in ART: Adaptive resonance theory (ART) has
beenwidelyexploredforbothacademiaandindustrialengineeringapplications,
with its fundamental papers cited over 13k times. Initiatives presented in this
bookonthediscoveryandtheoreticaldemonstrationofthelearningmechanism
of ART for clustering will attract researchers and practitioners working with
ART in related areas, such as computer science, cognitive science, and
neuroscience.
3. ExtensibleResearchBasis:Thisbookillustratestrajectoriesonhowtodevelop
ART-based clustering algorithms for handling different social media clustering
challenges, in terms of motivation, methodology, theoretical foundations, and
theirassociations.Itwillhelpreadersfullyunderstandtheresearchintentionsof
this book and form a basis for researchers to follow and provide their own
contributions.
4. Practical Technical Solutions: Driven by real-world challenges, this book
illustrates ART-based algorithms using real-world applications with experi-
mentaldemonstration.Readerswillsystematicallylearnstep-by-stepprocedures
viii Preface
to tackle real-world problems in social media data clustering, in terms of
algorithm design, implementation tradeoffs, and engineering considerations.
Therefore, this book will be interesting to researchers and practitioners,
searching for technical solutions for quick research and project setup.
Singapore Lei Meng
Singapore Ah-Hwee Tan
Rolla, USA Donald C. Wunsch II
Acknowledgments
ThisresearchissupportedinpartbytheNationalResearchFoundation,PrimeMinister’s
Office, Singapore under its IDM Futures Funding Initiative and administered by the
Interactive and Digital Media Programme Office; the Ministry of Education Academic
Research Fund (MOE AcRF), Singapore, the DSO National Laboratories, Singapore
under research grant numbers DSOCL11258 and DSOCL16006; and the National
Research Foundation, Prime Ministers Office, Singapore under its IRC@Singapore
Funding Initiative.
PartialsupportforthisresearchisalsoreceivedfromtheMissouriUniversityof
Science and Technology Intelligent Systems Center, the Mary K. Finley Missouri
Endowment,theLifelongLearningMachinesprogramfromDARPA/Microsystems
Technology Office, and the Army Research Laboratory (ARL); and it was
accomplished under Cooperative Agreement Number W911NF-18-2-0260. The
views and conclusions contained in this document are those of the authors and
should not be interpreted as representing the official policies, either expressed or
implied, of the Army Research Laboratory or the U.S. Government. The U.S.
Government is authorized to reproduce and distribute reprints for Government
purposes notwithstanding any copyright notation herein.
TheauthorswouldliketothanktheNTU-UBCResearchCenterofExcellencein
Active Living for the Elderly (LILY), the School of Computer Science and
Engineering (SCSE), Nanyang Technological University (NTU), and the
DepartmentofElectrical&ComputerEngineer,MissouriUniversityofScienceand
Technology(MissouriS&T),fortheireffortsinprovidinganidealenvironmentfor
research.Theauthorswouldalsoliketoexpressanintellectualdebtofgratitudefor
many generous mentors, especially Stephen Grossberg and Gail Carpenter for the
development of Adaptive Resonance Theory and related neural networks archi-
tectures upon which this work is built.
ix