Data mining

This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.

This book discusses different aspects of group recommender systems, which are systems that help to identify recommendations for groups instead of single users. In this context, the authors present different related techniques and applications. The book includes in-depth summaries of group recommendation algorithms, related industrial applications, different aspects of preference construction and explanations, user interface aspects of group recommender systems, and related psychological aspects that play a crucial role in group decision scenarios.

The concept of swarm intelligence at first originated from the observation of nature. Through the observation and study of the behaviour of swarms of living creatures as ants colony, bird flocks, bees colony and fish school, inspired by the swarm/social phenomena exhibited by these biological swarms, the swarm of simple individuals through mutual cooperation shows up the emergence phenomena at the level of swarm, that is, 'the swarm of simple individuals shows the characteristics of complex intelligent behaviour through cooperation.'The swarm intelligence algorithms are characterised of simplicity, uncertainty, interactivity, distributed parallelism, robustness, scalability, and self-organisation. At present, the study of swarm intelligence algorithms mainly includes theory, algorithm and application. Its development trends include developing hybrid algorithms, new improved algorithms and theoretical analysis as well as solving large-scale problems (big data application). In general, swarm intelligence algorithms may shed a light on breaking the curse of no free lunches (NFLs), which shows that a deep study might give us enough anticipation motivating more and more researchers to engage in the research of swarm intelligence algorithms and their applications.Thousands of papers are published each year presenting new algorithms, new improvements and numerous real world applications. This makes it hard for researchers and students to share their ideas with other colleagues; follow up the works from other researchers with common interests; and to follow new developments and innovative approaches. This complete and timely collection fills this gap by presenting the latest research systematically and thoroughly to provide readers with a full view of the field of swarm. Students will learn the principles and theories of typical swarm intelligence algorithms; scholars will get inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions.Volume 1 contains 20 chapters and presents the basic principles and current algorithms and methods of well-known swarm intelligence algorithms and efficient improvements from typical particle swarm optimisation (PSO), ant colony optimisation (ACO) and fireworks algorithm (FWA) as well as other swarm intelligence algorithms for swarm robotics.With contributions from an international selection of leading researchers, Swarm Intelligence is essential reading for engineers, researchers, professionals and practitioners with interests in swarm intelligence working in the fields of computer science, information technology, artificial intelligence, neural networks, computational intelligence, bioengineering, physics, mathematics, and social sciences.

This book constitutes the proceedings of the 21st Australasian Conference on Data Science and Machine Learning, AusDM 2023, held in Auckland, New Zealand, during December 11¿13, 2023.The 20 full papers presented in this book were carefully reviewed and selected from 50 submissions. The papers are organized in the following topical sections: research track and application track. They deal with topics around data science and machine learning in everyday life.

This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2023), organized by Soft Computing Research Society (SCRS) and Malaviya National Institute of Technology Jaipur, India, from 14 to 15 July 2023. The book is divided into four volumes, and it covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.

This book constitutes the refereed proceedings of the 5th International Conference on Science of Cyber Security, SciSec 2023, held in Melbourne, VIC, Australia, during July 11¿14, 2023. The 21 full papers presented together with 6 short papers were carefully reviewed and selected from 60 submissions. The papers are organized in the topical sections named: ¿ACDroid: Detecting Collusion Applications on Smart Devices; Almost Injective and Invertible Encodings for Jacobi Quartic Curves; Decompilation Based Deep Binary-Source Function Matching.

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications. 

This book constitutes the proceedings of the 24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023, held in Évora, Portugal, during November 22¿24, 2023.The 45 full papers and 4 short papers presented in this book were carefully reviewed and selected from 77 submissions. IDEAL 2023 is focusing on big data challenges, machine learning, deep learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models, agents and hybrid intelligent systems, and real-world applications of intelligence techniques and AI.The papers are organized in the following topical sections: main track; special session on federated learning and (pre) aggregation in machine learning; special session on intelligent techniques for real-world applications of renewable energy and green transport; and special session on data selection in machine learning.

This book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios.  In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling.  Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years.  Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items.  This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets.  This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items.  To deal with the challenging task of outfit compatibility modeling, this book provides comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning.  Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers.  

This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas.  All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms.   As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy.  Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy,  Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels,  and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

This book presents research in big data analytics (BDA) for business of all sizes. The authors analyze problems presented in the application of BDA in some businesses through the study of development methodologies based on the three approaches ¿ 1) plan-driven, 2) agile and 3) hybrid lightweight. The authors first describe BDA systems and how they emerged with the convergence of Statistics, Computer Science, and Business Intelligent Analytics with the practical aim to provide concepts, models, methods and tools required for exploiting the wide variety, volume, and velocity of available business internal and external data - i.e. Big Data ¿ and provide decision-making value to decision-makers. The book presents high-quality conceptual and empirical research-oriented chapters on plan-driven, agile, and hybrid lightweight development methodologies and relevant supporting topics for BDA systems suitable to be used for large-, medium-, and small-sized business organizations.

This book constitutes the refereed proceedings of the International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) which was held in Jeju, Korea in August, 2023. The papers of this volume are organized in topical sections on wired and wireless communication systems, high dimensional data representation and processing, networks and information security, computing techniques for efficient networks design, electronic circuits for communication systems.

This brief highlights the factors associated with good goalkeeping techniques and their impact on goalkeepers¿ performance in elite European football leagues. The goalkeeping performances of 1600 goalkeepers from five consecutive seasons across the English Premier League, Spanish La Liga, Italian Serie A, and German Bundesliga are studied. The findings from this brief are useful for identifying the success metrices of top-class goalkeepers that help stakeholders to devise strategies to further enhance their performances and empower talent identification experts with pertinent information for mapping out future high-performance goalkeepers.

This book presents novel work of academicians, researchers, industry professionals, practitioners, and budding engineers to disseminate the most recent innovations, trends, and concerns along with the present-day challenges and the solving approaches for implementation in the domains of data science, intelligent computing, and computer networks and security. It is a collection of selected high-quality research papers from the International Conference on Data Science, Intelligent Computing and Cyber Security (ICDIC 2020) organized by Sree Vidyanikethan Engineering College, Tirupati, India, during 27-29 February 2020. It discusses the latest challenges and solutions in the field of data innovation, data management, data analysis, data security, and intelligent methods and applications.

This book constitutes the proceedings of the 19th International Workshop on Security and Trust Management, STM 2023, co-located with the 28th European Symposium on Research in Computer Security, ESORICS 2023, held in The Hague, The Netherlands, during September 28th, 2023 The 5 full papers together with 4 short papers included in this volume were carefully reviewed and selected from 15 submissions. The workshop presents papers with topics such as security and privacy, trust models, security services, authentication, identity management, systems security, distributed systems security, privacy-preserving protocols.

This book provides a new model to explore discoverability and enhance the meaning of information. The authors have coined the term epidata, which includes items and circumstances that impact the expression of the data in a document, but are not part of the ordinary process of retrieval systems.  Epidata affords pathways and points to details that cast light on proximities that might otherwise go unknown.  In addition, epidata are clues to mis-and dis-information discernment.  There are many ways to find needed information; however, finding the most useable information is not an easy task.  The book explores the uses of proximity and the concept of epidata that increases the probability of  finding functional information.  The authors sketch a constellation of proximities, present examples of attempts to accomplish proximity, and provoke a discussion of the role of proximity in the field. In addition, the authors suggest that proximity is a thread between retrieval constructs based on known topics, predictable relations, and types of information seeking that lie outside constructs such as browsing, stumbling, encountering, detective work, art making, and translation.

In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Klling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that  concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.  

This book constitutes the refereed proceedings of 7 workshops, held at the 42nd International Conference on Conceptual Modeling, ER 2023, held in Lisbon, Portugal, during November 6-9, 2023.The 28 full and 2 short papers were carefully reviewed and selected out of 53 submissions. Topics of interest span the entire spectrum of conceptual modeling, including research and practice in areas such as theories of concepts and ontologies, techniques for transforming conceptual models into effective implementations, and methods and tools for developing and communicating conceptual models. The following workshops are included in this volume: CMLS ¿ 4th International Workshop on Conceptual Modeling for Life Sciences;CMOMM4FAIR ¿ Third Workshop on Conceptual Modeling, Ontologies and (Meta)data Management for Findable, Accessible, Interoperable, and Reusable (FAIR) Data;EmpER ¿ 6th International Workshop on Empirical Methodsin Conceptual Modeling;JUSMOD ¿ Second International Workshop on Digital Justice, Digital Law and Conceptual Modeling;OntoCom ¿ 9th International Workshop on Ontologies and Conceptual Modeling;QUAMES ¿ 4th International Workshop on Quality and Measurement of Model-Driven Software Development;SmartFood ¿ First Workshop on Controlled Vocabularies and Data Platforms for Smart Food Systems.

This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9¿11, 2023.The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.