Book ChapterDOI
A contribution to decision tree construction based on rough set theory
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The algorithm of building a decision tree is introduced by comparing the information gain or entropy and using the algorithm, the complexity of decision Tree is decreased, the construction of decisionTree is optimized and the rule of data mining could be built.Abstract:
In this paper, the algorithm of building a decision tree is introduced by comparing the information gain or entropy. The produced process of univariate decision tree is given as an example. According to rough sets theory, the method of constructing multivariate decision tree is discussed. Using the algorithm, the complexity of decision tree is decreased, the construction of decision tree is optimized and the rule of data mining could be built.read more
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Proceedings Article
SPRINT: A Scalable Parallel Classifier for Data Mining
TL;DR: A new decision-tree-based classification algorithm, called SPRINT, is presented that removes all of the memory restrictions, and is fast and scalable, and designed to be easily parallelized, allowing many processors to work together to build a single consistent model.
Book ChapterDOI
SLIQ: A Fast Scalable Classifier for Data Mining
TL;DR: Issues in building a scalable classifier are discussed and the design of SLIQ, a new classifier that uses a novel pre-sorting technique in the tree-growth phase to enable classification of disk-resident datasets is presented.
Journal ArticleDOI
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
TL;DR: This paper presents a unifying framework called Rain Forest for classification tree construction that separates the scalability aspects of algorithms for constructing a tree from the central features that determine the quality of the tree.