Titlebook: Proactive Data Mining with Decision Trees; Haim Dahan,Shahar Cohen,Oded Maimon Book 2014 The Author(s) 2014 active learning.data mining.de

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Introduction to Proactive Data Mining,In this chapter, we provide an introduction to the aspects of the exciting field of data mining, which are relevant to this book. In particular, we focus on classification tasks and on decision trees, as an algorithmic approach for solving classification tasks.

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Proactive Data Mining in the Real World: Case Studies,This chapter presents two real world implementations of the proactive data mining, using decision trees from two different sectors: cellular services (Sect. 4.1) and security (Sect. 4.2). Using actual datasets, we address real problems that two companies in these business areas face.

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Conclusions,Most works on data mining focus on methods for extracting patterns from datasets containing data from the past or previous events. Although useful, the ability to extract patterns by itself does not provide a holistic answer to what businesses really need—optimization rather than merely discovery.

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Haim Dahan,Shahar Cohen,Oded MaimonIncludes supplementary material:

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https://doi.org/10.1007/978-1-4939-0539-3active learning; data mining; decision trees; maximal-utility splitting criterion; optimization

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