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Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching

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This volume constitutes the proceedings of the 18th Industrial Conference on Adances in Data Mining, ICDM 2018, held in New York, NY, USA, in July 2018. The 24 regular papers presented in this book were carefully reviewed and selected from 146 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine and agriculture, and in process control, industry, and society.

Computer science. --- Data mining. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Informatics --- Science --- Data mining

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This book discusses the development of a theory of info-statics as a sub-theory of the general theory of information. It describes the factors required to establish a definition of the concept of information that fixes the applicable boundaries of the phenomenon of information, its linguistic structure and scientific applications. The book establishes the definitional foundations of information and how the concepts of uncertainty, data, fact, evidence and evidential things are sequential derivatives of information as the primary category, which is a property of matter and energy. The sub-definitions are extended to include the concepts of possibility, probability, expectation, anticipation, surprise, discounting, forecasting, prediction and the nature of past-present-future information structures. It shows that the factors required to define the concept of information are those that allow differences and similarities to be established among universal objects over the ontological and epistemological spaces in terms of varieties and identities. These factors are characteristic and signal dispositions on the basis of which general definitional foundations are developed to construct the general information definition (GID). The book then demonstrates that this definition is applicable to all types of information over the ontological and epistemological spaces. It also defines the concepts of uncertainty, data, fact, evidence and knowledge based on the GID. Lastly, it uses set-theoretic analytics to enhance the definitional foundations, and shows the value of the theory of info-statics to establish varieties and categorial varieties at every point of time and thus initializes the construct of the theory of info-dynamics.

Information theory. --- Communication theory --- Engineering. --- Data mining. --- Computational intelligence. --- Computational Intelligence. --- Data Mining and Knowledge Discovery. --- Information and Communication, Circuits. --- Communication --- Cybernetics --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Construction --- Industrial arts --- Technology --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Mathematics. --- Math --- Science

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This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.

Big data. --- Data mining. --- Data sets, Large --- Large data sets --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Engineering. --- Computational intelligence. --- Computational Intelligence. --- Data Mining and Knowledge Discovery. --- Database searching --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Construction --- Industrial arts --- Technology --- Data sets

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The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions. These days we find many computer programs that exhibit various useful learning methods and commercial applications. Goal of machine learning is to develop computer programs that can learn from experience. Machine learning involves knowledge from various disciplines like, statistics, information theory, artificial intelligence, computational complexity, cognitive science and biology. For problems like handwriting recognition, algorithms that are based on machine learning out perform all other approaches. Both machine learning and data science are interrelated. Data science is an umbrella term to be used for techniques that clean data and extract useful information from data. In field of data science, machine learning algorithms are used frequently to identify valuable knowledge from commercial databases containing records of different industries, financial transactions, medical records, etc. The main objective of this book is to provide an overview on latest advancements in the field of machine learning and data science, with solutions to problems in field of image, video, data and graph processing, pattern recognition, data structuring, data clustering, pattern mining, association rule based approaches, feature extraction techniques, neural networks, bio inspired learning and various machine learning algorithms. .

Engineering. --- Big data. --- Data mining. --- Computational intelligence. --- Computational Intelligence. --- Data Mining and Knowledge Discovery. --- Big Data. --- Big Data/Analytics. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data sets, Large --- Large data sets --- Construction --- Industrial arts --- Technology --- Machine learning --- COMPUTERS / General. --- Data sets --- Intelligence, Computational --- Artificial intelligence --- Soft computing