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Download The Top Ten Algorithms in Data Mining by Vipin Kumar, Xindong Wu PDF

By Vipin Kumar, Xindong Wu

Deciding on the most influential algorithms which are regularly occurring within the facts mining neighborhood, The best Ten Algorithms in information Mining offers an outline of every set of rules, discusses its impression, and studies present and destiny examine. completely evaluated via self sustaining reviewers, every one bankruptcy specializes in a specific set of rules and is written by means of both the unique authors of the set of rules or world-class researchers who've generally studied the respective algorithm.

The booklet concentrates at the following very important algorithms:
C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.

Examples illustrate how every one set of rules works and spotlight its performance in a real-world program. The textual content covers key topics—including type, clustering, statistical studying, organization research, and hyperlink mining—in info mining learn and improvement in addition to in info mining, computing device studying, and synthetic intelligence courses.

By naming the best algorithms during this box, this ebook encourages using info mining thoughts in a broader realm of real-world purposes. it's going to encourage extra facts mining researchers to additional discover the influence and novel study problems with those algorithms.

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Extra info for The Top Ten Algorithms in Data Mining

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Accuracy) on the training data, but also guarantees high predictive accuracy for the future data from the same distribution as the training data. 1 Illustration of the optimal hyperplane in SVC for a linearly separable case. Intuitively, a margin can be defined as the amount of space, or separation, between the two classes as defined by a hyperplane. Geometrically, the margin corresponds to the shortest distance between the closest data points to any point on the hyperplane. 1 illustrates a geometric construction of the corresponding optimal hyperplane under the above conditions for a two-dimensional input space.

Theoretical Foundations . . . . . . . . . . . . . . . . . . . . . . . . . Support Vector Regressor . . . . . . . . . . . . . . . . . . . . . . . . Software Implementations . . . . . . . . . . . . . . . . . . . . . . . . Current and Future Research . . . . . . . . . . . . . . . . . . . . . . 1 Computational Efficiency . . . . . . . . . . . . . . . . . . . . 2 Kernel Selection . . .

5 Advanced Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 21 22 26 27 30 32 33 34 Introduction In this chapter, we describe the k-means algorithm, a straightforward and widely used clustering algorithm.

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