Method and system for classifying data by adopting decision tree

A data classification and decision tree technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as difficulties, inability to handle massive data, uncontrollable iterative process, etc., and achieve the effect of controllable times

Active Publication Date: 2011-10-12
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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Problems solved by technology

[0009] Therefore, many implementations of existing classification decision trees are serial and memory-based, so they cannot handle massive amounts of data; and for existing distributed processing methods, although the data processing scale has been greatly improved, the programming implementation i

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  • Method and system for classifying data by adopting decision tree
  • Method and system for classifying data by adopting decision tree
  • Method and system for classifying data by adopting decision tree

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Embodiment Construction

[0040] In order to make the purpose, technical solution and advantages of the present invention clearer, a data classification method and system using a decision tree of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0041] A kind of data classification method and system adopting decision tree of the present invention, through training data, construct decision tree based on MapReduce mechanism, the intermediate node of tree is split decision attribute, and the leaf node of tree all has category mark, so from root node To the leaf node constitutes a discriminant rule. After the classification decision tree is constructed, the test samples can be classified.

[0042] The parallel classification decision tree algorithm adopted in the present invent...

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Abstract

The invention discloses a method and system for classifying data by adopting a decision tree. The method comprises the following steps of: parallel computing the information gain of each attribute in training data based on a MapReduce mechanism, and selecting optimum division decision attributes as nodes to construct the decision tree; based on the decision tree, classifying input data records. Aparallel decision tree ID3 algorithm is realized based on the MapReduce. A large data set can be processed, and the parallel efficiency is high. The parallel computing is realized for the nodes in the decision tree and the nodes in the same layer.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a data classification method and system using a decision tree. Background technique [0002] Classification is an important topic in data mining. The purpose of classification is to learn a classification function or classification model (also often referred to as a classifier), which can map data items in the database to one of the given categories. Classification can be used to extract models describing important classes of data or to predict future data trends. The purpose of classification is to analyze the input data and find an accurate description or model for each class through the characteristics of the data in the training set. Such descriptions are often expressed using predicates. The resulting class descriptions are used to classify future test data. Although the class labels of these future test data are unknown, we can still predict the class to which these...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 庄福振何清
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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