data mining chapter mining stream

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  Algorithms for Distributed Data Stream Mining | SpringerLink

  This chapter presents an introduction to distributed data mining for continuous streams. It focuses on the situations where the data observed at different locations change with time. The chapter provides an exposure to the literature and illustrates the behavior of this class of algorithms by exploring two very different types of techniques ...

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  Chapter 1: Introduction to Data Mining - University of Alberta

  1999922 · Data mining allows the discovery of knowledge potentially useful and unknown. Whether the knowledge discovered is new, useful or interesting, is very subjective and depends upon the application and the user. It is certain that data mining can generate, or discover, a very large number of patterns or rules.

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  Proceedings of the 2015 SIAM International Conference on Data Mining

  2024531 · Abstract Data stream classification algorithms for nonstationary environments frequently assume the availability of class labels, instantly or with some lag after the classification. However, certain applications, mainly those related to sensors and robotics, involve high costs to obtain new labels during the classification phase. Such a …

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  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data ...

  It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

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  Data Mining: Concepts and Techniques (The Morgan …

  201176 · Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges.

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  Slides by Chapters: Han, Pei and Tong: Data Mining

  2023529 · Slides in PowerPoint Chapter 1: Introduction Chapter 2: Data, measurements, and data preprocessing Chapter 3: Data warehousing and online analytical processing Chapter 4: Pattern mining: basic concepts and methods Chapter 5: Pattern mining: advanced methods Chapter 6: Classification: basic concepts and methods …

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  (PDF) Stream Data Mining - ResearchGate

  2004725 · Data mining involves an integration of techniques from multiple disciplines. such as database technology, statistics, machine learning, neural networks, information retrieval, etc [3]. According ...

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  Mining Concept-Drifting Data Streams | SpringerLink

  201011 · Knowledge discovery from infinite data streams is an important and difficult task. We are facing two challenges, the overwhelming volume and the concept drifts of the streaming data. In this chapter, we introduce a general framework for …

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  Sequential Patterns Mining on High-Dimensional Data Stream

  Sequential pattern mining is an important problem in many data mining applications. When the data comes as stream, its mining becomes difficult. Unlike relational database the stream data size keep growing while the memory size is fixed relatively. So it is...

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  Classification (Chapter 5) - Data Mining and Data Warehousing

  202445 · Chapter Objectives. To comprehend the concept, types and working of classification. To identify the major differences between classification and regression problems. To become familiar about the working of classification. To introduce the decision tree classification system with concepts of information gain and Gini Index.

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