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Data.

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The IEEE International Conference on Data Mining series (ICDM) has established itself as the world s premier research conference in data mining It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences The conference covers all aspects of data mining, including algorithms, software and systems, and applications ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge based systems, and high performance computing By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state of the art in data mining.

Data mining. --- Data mining

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The massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.

Big data. --- Big data

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Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding.

Data mining --- Data warehousing

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Diese Arbeit hat sich zum Ziel gesetzt, Methoden aufzuzeigen, "Big-Data"-Archive zu organisieren und zentrale Elemente der enthaltenen Informationen zu visualisieren. Anhand von drei wissenschaftlichen Experimenten werde ich zwei "Big-Data"- Herausforderungen, Datenvolumen (Volume) und Heterogenität (Variety), untersuchen und eine Visualisierung im Browser präsentieren, die trotz reduzierter Datenrate die wesentliche Information in den Datensätzen enthält. The scope of this research focuses on managing Big Data and eventually visualising the core information of the data itself. Specifically, I study three large-scale experiments that feature two Big Data challenges: large data size (Volume) and heterogeneous data (Variety), and provide the final visualisation through the web browser in which the size of the input data has to be reduced while preserving the vital information.

Big data. --- Big data