Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics
Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics
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DOI：
10.1007/978-1-84800-201-2
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发表时间：
2008-08
期刊：
影响因子：
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通讯作者：
D. Simovici;C. Djeraba
中科院分区：
文献类型：
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作者：
D. Simovici;C. Djeraba
数据挖掘文献包含许多优秀的标题，满足用户的需求，从决策到生物数据的模式调查。然而，这些书并没有涉及数据挖掘研究人员和博士生目前所需要的数学工具，我们认为现在是时候制作一个新版本的书，将数据挖掘的数学与其应用相结合。我们强调，这本书是关于数据挖掘的数学工具，而不是关于数据挖掘本身;尽管如此，数据挖掘中的数学概念的许多实质性应用都包括在内。这本书的目的是作为一个参考工作的数据矿工。我们提出了几个领域的数学，在我们看来是至关重要的数据挖掘：集理论，包括偏序集和组合学;线性代数，其在线性算法中的许多应用;拓扑结构，用于理解和结构化数据，和图论，提供了一个强大的工具，用于构建数据模型。
The data mining literature contains many excellent titles that address the needs of users with a variety of interests ranging from decision making to pattern investigation in biological data. However, these books do not deal with the mathematical tools that are currently needed by data mining researchers and doctoral students and we felt that it is timely to produce a new version of our book that integrates the mathematics of data mining with its applications. We emphasize that this book is about mathematical tools for data mining and not about data mining itself; despite this, many substantial applications of mathematical concepts in data mining are included. The book is intended as a reference for the working data miner. We present several areas of mathematics that, in our opinion are vital for data mining: set theory, including partially ordered sets and combinatorics; linear algebra, with its many applications in linear algorithms; topology that is used in understanding and structuring data, and graph theory that provides a powerful tool for constructing data models.