Titlebook: Data Mining for Service; Katsutoshi Yada Book 2014 Springer-Verlag Berlin Heidelberg 2014 Data Mining.Domain Knowledge.Large Database.Sens

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Commentar zur Pharmacopoea Germanicaant to generate a display (or users may choose any three orthogonal axes). We conducted implementation studies to demonstrate the value of our system with an artificial data set and a de facto benchmark news article dataset from the United States NIST Text REtrieval Competitions (TREC).

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Data Mining for Servicereasingly important in various fields [., .]. In developed countries, service industries comprise a very high percentage of GDP, and even in manufacturing, in order to gain a competitive advantage, there is a focus on services which create added value.

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Feature Selection Over Distributed Data Streamsg the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most of the existing algorithms deal with monitoring simple aggregated values such as frequency of occurrence of stream items, motivated by recen

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Learning Hidden Markov Models Using Probabilistic Matrix Factorizationrameters of a HMM are estimated using the Baum–Welch algorithm, which scales linearly with the sequence length and quadratically with the number of hidden states. In this chapter, we propose a significantly faster algorithm for HMM parameter estimation. The crux of the algorithm is the probabilistic

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Dimensionality Reduction for Information Retrieval Using Vector Replacement of Rare Termse introduce a new approach to dimensionality reduction for text retrieval. According to Zipf’s law, the majority of indexing terms occurs only in a small number of documents. Our new algorithm exploits this observation to compute a dimensionality reduction. It replaces rare terms by computing a vect