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2014 | Buch

Introduction to Proactive Data Mining Kapitel lesen Erstes Kapitel lesen

Proactive Data Mining with Decision Trees

verfasst von: Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon

Verlag: Springer New York

Buchreihe : SpringerBriefs in Electrical and Computer Engineering

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting criterion for decision trees, termed maximal-utility, which maximizes the potential for enhancing profitability in the output tree. Two real-world case studies, one of a leading wireless operator and the other of a major security company, are also included and demonstrate how applying the proactive approach to classification tasks can solve business problems. Proactive Data Mining with Decision Trees is intended for researchers, practitioners and advanced-level students.

Inhaltsverzeichnis

Frontmatter
1. Introduction to Proactive Data Mining
Abstract
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.
Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
2. Proactive Data Mining: A General Approach and Algorithmic Framework
Abstract
In the previous section we presented several important data mining concepts. In this chapter, we argue that with many state-of-the-art methods in data mining, the overly-complex responsibility of deciding on this action or that is left to the human operator. We suggest a new data mining task, proactive data mining. This approach is based on supervised learning, but focuses on actions and optimization, rather than on extracting accurate patterns. We present an algorithmic framework for tackling the new task. We begin this chapter by describing our notation.
Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
3. Proactive Data Mining Using Decision Trees
Abstract
In the previous chapter we introduced the task of proactive data mining and sketched an algorithmic framework for solving the task: first build a prediction model and then use it for optimization. In this chapter, we focus on decision tree classifiers and describe in detail two possible ways of implementing proactive data mining using: (a) a ready-made decision tree algorithm, and (b) a novel decision tree algorithm. We designed this latter algorithm to support the optimization phase of the proposed framework.
Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
4. Proactive Data Mining in the Real World: Case Studies
Abstract
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.
Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
5. Sensitivity Analysis of Proactive Data Mining
Abstract
As stated in Chap. 4, to achieve an effective and applicable solution for a data mining problem, it is vital to thoroughly understand the problem at hand, in particular its constraints, environment and its problem specific knowledge. However, it is difficult to pinpoint the exact knowledge (i.e., attribute values) necessary for optimally implementing the proactive data mining method. In this chapter we present several scenarios over the security company’s case (Chap. 4) to demonstrate the general boundaries of the method.
Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
6. Conclusions
Abstract
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.
Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
Metadaten
Titel
Proactive Data Mining with Decision Trees
verfasst von
Haim Dahan
Shahar Cohen
Lior Rokach
Oded Maimon
Copyright-Jahr
2014
Verlag
Springer New York
Electronic ISBN
978-1-4939-0539-3
Print ISBN
978-1-4939-0538-6
DOI
https://doi.org/10.1007/978-1-4939-0539-3