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R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

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Data mining

Data mining is the discovery of a model in data; it's also called exploratory data analysis, and discovers useful, valid, unexpected, and understandable knowledge from the data. Some goals are shared with other sciences, such as statistics, artificial intelligence, machine learning, and pattern recognition. Data mining has been frequently treated as an algorithmic problem in most cases. Clustering, classification, association rule learning, anomaly detection, regression, and summarization are all part of the tasks belonging to data mining.

The data mining methods can be summarized into two main categories of data mining problems: feature extraction and summarization.

Feature extraction

This is to extract the most prominent features of ...

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