By Nong Ye
New applied sciences have enabled us to gather sizeable quantities of information in lots of fields. even though, our speed of studying helpful info and data from those facts falls a long way at the back of our speed of accumulating the information. Data Mining: Theories, Algorithms, and Examples introduces and explains a entire set of information mining algorithms from quite a few info mining fields. The e-book studies theoretical rationales and procedural info of knowledge mining algorithms, together with these as a rule present in the literature and people providing substantial hassle, utilizing small info examples to give an explanation for and stroll in the course of the algorithms.
The ebook covers quite a lot of facts mining algorithms, together with these as a rule present in info mining literature and people no longer absolutely coated in so much of latest literature as a result of their substantial trouble. The e-book provides a listing of software program programs that aid the knowledge mining algorithms, functions of the knowledge mining algorithms with references, and routines, besides the options handbook and PowerPoint slides of lectures.
The writer takes a realistic method of info mining algorithms in order that the information styles produced may be absolutely interpreted. This procedure permits scholars to appreciate theoretical and operational points of information mining algorithms and to manually execute the algorithms for a radical figuring out of the knowledge styles produced by means of them.
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Extra info for Data Mining : Theories, Algorithms, and Examples
Space, (x, y), with y = 2x and x = 1, 2, …, 10. This two-dimensional data set can be represented as the one-dimensional data set with z as the axis, and z is related to the original variables, x and y, as follows: 2 y z = x * 12 + 1 * . 361. Part IV of the book introduces the following data mining algorithms that are used to discover data reduction patterns from data: • Principal component analysis in Chapter 14 • Multidimensional scaling in Chapter 15 Chapters 23 and 8 in The Handbook of Data Mining (Ye, 2003) give applications of principal component analysis to volcano data and science and engineering data.
Since one variable gives one dimension of data, data reduction patterns allow a data set in a high-dimensional space to be represented in a low-dimensional space. 4 Reduction of a two-dimensional data set to a one-dimensional data set. space, (x, y), with y = 2x and x = 1, 2, …, 10. This two-dimensional data set can be represented as the one-dimensional data set with z as the axis, and z is related to the original variables, x and y, as follows: 2 y z = x * 12 + 1 * . 361. Part IV of the book introduces the following data mining algorithms that are used to discover data reduction patterns from data: • Principal component analysis in Chapter 14 • Multidimensional scaling in Chapter 15 Chapters 23 and 8 in The Handbook of Data Mining (Ye, 2003) give applications of principal component analysis to volcano data and science and engineering data.
Except the root node and leaf nodes, all other nodes in the decision trees are called internal nodes. The decision tree can classify a data record by passing the data record through the decision tree using the attribute values in the data record. For example, the data record for instance 10 is first checked with the first split condition at the root node. With x7 = 0, the data record is passed down to the left branch. With x8 = 0 and then x9 = 0, the data record is passed down to the left-most leaf node.