Đang chuẩn bị liên kết để tải về tài liệu:
Data Mining and Knowledge Discovery Handbook, 2 Edition part 18

Data Mining and Knowledge Discovery Handbook, 2 Edition part 18. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 150 Lior Rokach and Oded Maimon whereas leaves are denoted as triangles. Note that this decision tree incorporates both nominal and numeric attributes. Given this classifier the analyst can predict the response of a potential customer by sorting it down the tree and understand the behavioral characteristics of the entire potential customers population regarding direct mailing. Each node is labeled with the attribute it tests and its branches are labeled with its corresponding values. In case of numeric attributes decision trees can be geometrically interpreted as a collection of hyperplanes each orthogonal to one of the axes. Naturally decisionmakers prefer less complex decision trees since they may be considered more comprehensible. Furthermore according to Breiman et al. 1984 the tree complexity has a crucial effect on its accuracy. The tree complexity is explicitly controlled by the stopping criteria used and the pruning method employed. Usually the tree complexity is measured by one of the following metrics the total number of nodes total number of leaves tree depth and number of attributes used. Decision tree induction is closely related to rule induction. Each path from the root of a decision tree to one of its leaves can be transformed into a rule simply by conjoining the tests along the path to form the antecedent part and taking the leaf s class prediction as the class 9 Classification Trees 151 value. For example one of the paths in Figure 9.1 can be transformed into the rule If customer age is is less than or equal to or equal to 30 and the gender of the customer is Male - then the customer will respond to the mail . The resulting rule set can then be simplified to improve its comprehensibility to a human user and possibly its accuracy Quinlan 1987 . 9.2 Algorithmic Framework for Decision Trees Decision tree inducers are algorithms that automatically construct a decision tree from a given dataset. Typically the goal is to find the optimal decision tree .