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Description: 一份关于数据挖掘的入门资料，进行了数据挖掘的基本介绍
还介绍了聚类，关联规则，决策树，神经网络-data mining, cluster, classification, Decision tree, Neural network
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Size: 418816 |
Author: Erin |
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Description: decision tree
calculate information gain
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Size: 652288 |
Author: Aakashi |
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Description: decision tree on iris
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Size: 1541120 |
Author: Tao |
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Description: Recently, information security has become a key issue
in information technology as the number of computer security
breaches are exposed to an increasing number of security threats. A
variety of intrusion detection systems (IDS) have been employed for
protecting computers and networks from malicious network-based or
host-based attacks by using traditional statistical methods to new data
mining approaches in last decades. However, today s commercially
available intrusion detection systems are signature-based that are not
capable of detecting unknown attacks. In this paper, we present a
new learning algorithm for anomaly based network intrusion
detection system using decision tree algorithm that distinguishes
attacks from normal behaviors and identifies different types of
intrusions. Experimental results on the KDD99 benchmark network
intrusion detection dataset demonstrate that the proposed learning
algorithm achieved 98 detection rate (DR) in comparison with
other existing methods.-Recently, information security has become a key issue
in information technology as the number of computer security
breaches are exposed to an increasing number of security threats. A
variety of intrusion detection systems (IDS) have been employed for
protecting computers and networks from malicious network-based or
host-based attacks by using traditional statistical methods to new data
mining approaches in last decades. However, today s commercially
available intrusion detection systems are signature-based that are not
capable of detecting unknown attacks. In this paper, we present a
new learning algorithm for anomaly based network intrusion
detection system using decision tree algorithm that distinguishes
attacks from normal behaviors and identifies different types of
intrusions. Experimental results on the KDD99 benchmark network
intrusion detection dataset demonstrate that the proposed learning
algorithm achieved 98 detection rate (DR) in comparison with
other existing methods.
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Size: 312320 |
Author: keerthi |
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Description: its a tutorial for using WEKA J48 decision tree algorithm . the document language is persian
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Size: 2731008 |
Author: saeedOo |
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Description: 使用MATLAB语言完成的决策树算法。
里面有详细说明-Using the MATLAB language to complete the decision tree algorithm. Details inside
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Size: 87040 |
Author: 老虎 |
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Description: 实现ID3算法，在结果中以树表示出来。决策树是对数据进行分类，以此达到预测的目的。该决策树方法先根据训练集数据形成决策树，如果该树不能对所有对象给出正确的分类，那么选择一些例外加入到训练集数据中，重复该过程一直到形成正确的决策集。-ID3 algorithm to achieve, in the results that come out to the tree. Decision tree is to classify the data, thus achieving the purpose of prediction. The decision tree training set of data according to the formation of the first decision tree, if the tree can not give the correct classification of all objects, then select a number of exceptions to the training set data, repeat the process until the correct decision set.
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Size: 2048 |
Author: 王剑亭 |
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Description: Classification with decision tree using ID3 algorithm
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Size: 19456 |
Author: Mus |
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Description: decision tree implementation
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Size: 375808 |
Author: chinni |
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Description: 8 puzzle using a decision tree.
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Size: 2048 |
Author: ajue |
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Description: his algorithm was proposed by Quinlan (1993). The C4.5 algorithm generates a classification-decision tree for the given data-set by recursive partitioning of data. The decision is grown using Depth-first strategy. The algorithm considers all the possible tests that can split the data set and selects a test that gives the best information gain. For each discrete attribute, one test with outcomes as many as the number of distinct values of the attribute is considered. For each continuous attribute, binary tests involving every distinct values of the attribute are considered. In order to gather the entropy gain of all these binary tests efficiently, the training data set belonging to the node in consideration is sorted for the values of the continuous attribute and the entropy gains of the binary cut based on each distinct values are calculated in one scan of the sorted data. This process is repeated for each continuous attributes.
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Size: 2048 |
Author: rajesh |
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Description: 是一个决策树算法，该算法-Is a decision tree algorithm
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Size: 133120 |
Author: jj |
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Description: decision tree in matlab code
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Size: 9216 |
Author: sadegh |
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Description: FID3 Fuzzy Induction Decision Tr-FID3 Fuzzy Induction Decision Tree
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Size: 497664 |
Author: Artz |
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Description: 本程序实现了一个决策树生成程序， 决策树学习是应用最广的归纳推理算法之一。这是一种逼近离散值函数的方法，对噪声数据有很好的健壮性且能够学习析取表达式。在这种方法中学习到的函数被表示为一棵决策树。学习得到的决策树也能再被表示多个if-then规则，以提高可读性。本程序附带详细的说明文档-decision tree ID3
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Size: 51200 |
Author: 张 |
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Description: 决策树算法是利用最广泛的 数据挖掘算法之一-The decision tree algorithm is the most widely used data mining algorithms, one of...
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Size: 845824 |
Author: lixh |
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Description: 用决策树改进基于协议分析的入侵检测技术的实现-mprovement using decision tree based on protocol analysis, intrusion detection technology
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Size: 118784 |
Author: 君 |
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Description: 寻找新的构造决策树和简化决策树的方法一直是决策树技术研究的一个热点。SLIQ 算法和 SPRINT 算法很好的解决了磁盘驻留数据太大以至于无法被内存容纳带来的问题-Looking for new constructed decision tree and simplify the decision tree method has been a research focus of the decision tree technique. SLIQ algorithm and SPRINT algorithm to solve the disk-resident data is too large memory that they can not accommodate the problems caused by
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Size: 793600 |
Author: 毛玉凤 |
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Description: Decision tree learning algorithm has been successfully used in expert
systems in capturing knowledge. The main task performed in these systems is
using inductive methods to the given values of attributes of an unknown object
to determine appropriate classification according to decision tree rules.
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Size: 22528 |
Author: GasPro |
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Description: 基于决策树的增量学习算法相关文档，主要用于文本分类-Incremental learning algorithm based on decision tree document, mainly for text classification
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Size: 1107968 |
Author: liujingwen |
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