搜索资源列表
ecoli
- KDD的专业测试数据集ecoli，投KDD的可要同一使用他的数据集哈-KDD professional ecoli test data sets, the cast, until KDD he used the same data sets Kazakhstan
clusterglass
- KDD的专业测试数据集glass，投KDD的可要同一使用他的数据集哈-KDD professional glass test data sets, for me, KDD he used the same data sets Kazakhstan
KDD
- 核鉴别分析，MATLAB编写，可用于人脸识别等模式识别问题，欢迎使用。
Clustering.zip
- 数据挖掘算法的实现，基于模糊聚类的最大树算法，数据集是darpa99,也就是KDD-CUP99中采用的数据集,The realization of data mining algorithms, based on fuzzy clustering of the largest tree algorithm, a data set is darpa99, which is used in KDD-CUP99 data set
main
- KDD99的属性约简，国外高人写的代码。-KDD99 of attribute reduction, foreign Gaoren to write code.
rbf
- KDD中一个很重要的聚类方法之一，值得参考！-KDD in a very important one clustering method, it is also useful!
TPatternMiner
- Trajectory Pattern Mining-This software is an implementation of the T-Pattern mining algorithm. Reference paper is "Trajectory Pattern Mining", by F. Giannotti, M. Nanni, D. Pedreschi and F. Pinelli, published on KDD 2007 conference. This soft
192010k-average
- kmeans均值聚类算法:一种改进的基于半监督聚类的入侵检测算法ASCID（Active-learning Semi-supervised Clustering Intrusion Detection），-kmeans clustering algorithm Algorithm was simulated by KDD 99 datasets， which the experimental results demonstrate that ASCID algorithm can impro
fpGrowth
- 一个对数据挖掘中频繁模式挖掘算法FPgrowth的实现，且带有图形界面和介绍的文档-THE LUCS-KDD IMPLEMENTATIONS OF THE FP-GROWTH ALGORITHM
Apriori
- Aprior算法在matlab中的算法。编程语言为C++。APPRIOR算法是关联规则的一种算法-Aprior anology in kdd
Algo-genetic
- an implementation for the genetic algorithm to classify the intrusion in a network. it s tested on the KDD data network.
data-mining-technology
- 数据挖掘是知识发现过程的一个基本步 骤。KDD是一门交叉学科，它涉及统计学、数据库技术、计算机科学、模式识别、人工智能、机器学习等多个学科。 -Data mining is a fundamental step in the knowledge discovery process. KDD is an interdisciplinary, it involves statistics, database technology, computer science, pattern reco
Intrusion-Detection
- The problem of intrusion detection has been studied and received a lot of attention in machine learning and data mining in the literature survey. The existing techniques are not effective to improve the classification accuracy and to reduce high
matlab-data-mining
- 数据挖掘(Data Mining)阶段首先要确定挖掘的任务或目的。数据挖掘的目的就是得出隐藏在数据中的有价值的信息。数据挖掘是一门涉及面很广的交叉学科，包括器学习、数理统计、神经网络、数据库、模式识别、粗糙集、模糊数学等相关技术。它也常被称为“知识发现”。知识发现(KDD)被认为是从数据中发现有用知识的整个过程。数据挖掘被认为是KDD过程中的一个特定步骤，它用专门算法从数据中抽取模式(patter，如数据分类、聚类、关联规则发现或序列模式发现等。数据挖掘主要步骤是：数据准备、数据挖掘、结果的解释
Intrusion_detection
- 决策树算法，从mysql数据库中读取数据训练，KDD CUP 99的数据集，预测网络入侵。成功率在85 左右-The decision tree algorithm， get data from mysql for training KDD CUP 99 data sets, predict network intrusion. The success rate is about 85
aprioricsharp
- Apriori 数据挖掘算法的C#实现 数据库中的知识发现 (Knowledge Discovery in Databases,KDD) 是利用计算机自动地从海量信息中提取有用的知识 , 是一种有效利用信息的新方法 , 目前已成为数据库领域的研究热点之一。 KDD 的研究焦点在于数据挖掘。数据挖掘是从大型数据库或数据仓库中提取人们感兴趣的知识 , 这些知识是隐含的 , 事先未知的潜在的有用信息。主要包括的方法有 : 分类、回归分析、聚类、关联分析等 [1][5] 。关联规则的提取主要针对大型
Top-10-Algorithms-in-Data-Mining
- 在2006年9月召开的ICDM会议上，邀请了ACM KDD创新大奖(InnovationAward)和 Top 10 Algorithms in Data Mining IEEEICDM研究贡献奖(Research Contributions Award)的获奖者们来参与数据挖掘10大算 法的选举，每人提名10种他认为最重要的算法-Classification，Statistical Learning，Top 10 Algorithms in Data Mining，material
IJARCCE-96
- A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms
document
- Analysis of NSL-KDD Dataset for Fuzzy Based Intrusion Detection System
data--preprocessing-using-kdd-data-set
- Data Mining process model selected is KDD which starts selection of data.Initially the researcher has taken the Kddcup.data-10-perecnt which contains total of 311,027 records which includes both labeled and unlabeled records-Data Mining process model