Titlebook: Large-Scale Parallel Data Mining; Mohammed J. Zaki,Ching-Tien Ho Conference proceedings 2000 Springer-Verlag Berlin Heidelberg 2000 classi

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Parallel and Distributed Data Mining: An Introductiontion. This chapter presents a survey on large-scale parallel and distributed data mining algorithms and systems, serving as an introduction to the rest of this volume. It also discusses the issues and challenges that must be overcome for designing and implementing successful tools for large-scale data mining.

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Parallel Predictor Generationy in which predictors based on samples are built independently and hence concurrently. We discuss the performance implications for two subclasses: those in which predictors are independent, and those in which knowing a set of predictors reduces the difficulty of finding a new one.

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Collective, Hierarchical Clustering from Distributed, Heterogeneous Dataering and results comparing its performance with that of a centralized clustering algorithm are presented. An analysis of the algorithm complexity, in terms of overall computation time and communication requirements, is presented.

Vaginismus

https://doi.org/10.1007/3-540-46502-2classification; data mining; distributed information systems; information extraction; information retrie

在驾驶

Mohammed J. Zaki,Ching-Tien HoIncludes supplementary material:

Mere仅仅

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/l/image/581404.jpg

思想上升

978-3-540-67194-7Springer-Verlag Berlin Heidelberg 2000

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