Description: 分类知识的获取是数据挖掘要实现的重要任务之一，其核心问题是解决分类模型的构造和分类算法实现。本文以决策树分类方法中有代表性的方法C4．5为例，介绍数据挖掘中一种分类方法一决策树分类方法及其构建和应用研究。-Classification of knowledge acquisition is a data mining in order to achieve one of the important tasks, the core of the problem is classification model to solve the structure and classification algorithm. In this paper, decision tree classification method C4.5 has representation as an example, the introduction of data mining in a classification of one decision tree classification method and its building and applied research. Platform: |
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Author:梁旭 |
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Description: 数据挖掘是信息产业界近年来非常热门的研究方向，聚类分析是数据挖掘中的核心技术。本文对数据挖掘领域的聚类分析方法及代表算法进行分析，并从多个方面对这些算法性能进行比较，同时还对聚类分析在数据挖掘中的几个应用进行了阐述。-Data Mining is the IT industry is very popular in recent years the direction of the research, cluster analysis is data mining in the core technology. In this paper, the field of data mining methods and the representative of cluster analysis algorithm analysis, and from several aspects of these algorithms to compare the performance, but also on the cluster analysis of data mining in several applications are described. Platform: |
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Author:昂森 |
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Description: 聚类是数据挖掘中重要的研究课题。文章介绍了聚类，讨论了聚类分析中的数据类型及其相异度，概括了数据
挖掘中常用的聚类方法。最后，提出了聚类研究中今后的若干发展趋势。-Clustering is an important data mining research. In this paper, clustering, cluster analysis discussed in the different data types and their degrees, a summary of commonly used data mining clustering method. Finally, the clustering of a number of future research trends. Platform: |
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Author:李浩 |
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Description: 一个异常点是相当不同的或不符合一个数据集的其余部分数据。检测离群点是非常重要的许多应用中，并在最近引起了广泛关注 在数据挖掘研究界。在本文中，提出了一种方法检测发现异常数据的频繁模式（或频繁项目集-An outlier in a dataset is an observation or a point that is considerably
dissimilar to or inconsistent with the remainder of the data. Detection of
outliers is important for many applications and has recently attracted much attention
in the data mining research community. In this paper, we present a new
method to detect outliers by discovering frequent patterns (or frequent itemsets)
from the data set Platform: |
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Author:suivy |
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Description: 本文从基于数据库的知识发现开始，较系统全面地介绍了KDD、数据挖掘都基本概念，并研究了数据挖掘所采用都技术方法和应用领域：在现有研究工作的基础上，提出用连续量代替离散量来表示客体的思想，同时采用积分面积来表示两个客体之间都距离，并对客体进行分类。利用上述方法对学生都数据进行挖掘，得到了一些有用的结论，从而指导学校都教学工作。-Based on the knowledge discovety in database this paper presents a introduction of KDD,an concept of data mining and a research into the technical approach and fields of application of data mining.Based on the present research work,this paper is devoted to the idea of presenting the segregation amount by using the whole amount.Integral square is adopted to indicated the distance between two units.And the above-stated method is used to analyze the students data.And some useful information is thus obtained.So it is instructive to teaching at colleges. Platform: |
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Author:yunzhang |
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Description: This paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM,
Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification, Platform: |
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Author:sukmawati |
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Description: “机器学习”是人工智能的核心研究领域之一， 其最初的研究动机是为了让计算机系统具有人的学习能力以便实现人工智能，因为众所周知，没有学习能力的系统很难被认为是具有智能的。“数据挖掘”和“知识发现”通常被相提并论，并在许多场合被认为是可以相互替代的术语。据库界提供的技术来管理海量数据。
因为机器学习和数据挖掘有密切的联系，受主编之邀，本文把它们放在一起做一个粗浅的介绍。-" Machine learning" is the core research areas of artificial intelligence, its initial research motivation is to enable the computer system with a person' s ability to learn in order to achieve artificial intelligence, it is well known, there is no ability to learn the system can hardly be considered a smart . " Data mining" and " knowledge discovery" is often compared, and in many cases be considered as substitutes for one term. According to library community to provide technology to manage the vast amounts of data. Because of machine learning and data mining are closely linked, was invited by the editor of this paper, put them together to make a superficial introduction. Platform: |
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Author:cs |
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Description: This paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM,
Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification Platform: |
Size: 635904 |
Author:ParisM |
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Description: 关联规则论文：
GP在入侵检测规则提取中的适应度函数设计.pdf
采用数据挖掘的入侵检测技术研究.pdf
分类规则挖掘算法综述.pdf
-Articles of Association Rules: GP in intrusion detection rule extraction in the design of fitness function. Pdf intrusion detection using data mining technology research. Pdf Classification Rule Mining Algorithm. Pdf Platform: |
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Author:yxm |
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Description: 软件学报 2008年论文《聚类算法研究》，作者孙吉贵, 刘杰, 赵连宇。pdf格式，14页。对近年来聚类算法的研究现状与新进展进行归纳总结.一方面对近年来提出的较有代表性的聚类算法,从算法思想、关键技术和优缺点等方面进行分析概括 另一方面选择一些典型的聚类算法和一些知名的数据集,主要从正确率和运行效率两个方面进行模拟实验,并分别就同一种聚类算法、不同的数据集以及同一个数据集、不同的聚类算法的聚类情况进行对比分析.最后通过综合上述两方面信息给出聚类分析的研究热点、难点、不足和有待解决的一些问题.上述工作将为聚类分析和数据挖掘等研究提供有益的参考.
-The research actuality and new progress in clustering algorithm in recent years are summarized in this
paper. First, the analysis and induction of some representative clustering algorithms have been made from several
aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several
typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both
sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining. Platform: |
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Author:dengyue |
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Description: 基于WEKA平台的文本聚类研究与实现
文本聚类是文本挖掘领域的一个重要研究分支，是聚类方法在文本处理领域的应用。本文对基于空间向量模型的文本聚类过程做了较深入的讨论和总结，利用文本语料库，基于数据挖掘工具研究并实现了文本聚类的过程。本文首先给出了文本聚类的思想和过程，回顾了文本聚类领域的已有成果，列举了文本聚类领域在特征表示、特征提取等方面的基础研究工作。另外，本文回顾了现有的文本聚类算法，以及常用的文本聚类效果评价指标。在研究了已有成果的基础上，本文利用20 Newsgroup文本语料库，针对向量空间表示模型，在开源的数据挖掘平台WEKA上实现了文本预处理和k-means聚类算法，并根据实际聚类效果，就文本表示、特征选择、特征降维、等方面提出优化方案。-Text clustering is an important field of text mining research branch, is the clustering in the field of text processing applications. In this paper, based on vector space model for text clustering process to do a more in-depth discussion and summary, the use of the text corpus, based on data mining tools to study and realize the document clustering process. This paper shows the ideas and text clustering process, reviewed the existing text clustering results of the field, citing the field of document clustering in the feature representation, feature extraction and other aspects of basic research. In addition, the paper reviews the existing text clustering algorithm, as well as common text clustering validity. In the study has been based on the results, we use 20 Newsgroup corpus, for the vector space representation model, in the WEKA open source data mining platform to achieve a text preprocessing and k-means clustering algorithm, and according to the actual clustering effect to the tex Platform: |
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Author:yueyue |
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Description: 类的目的就是根据现有的图像特征建立一个分类器，能够对未知的图像类型进行预测。在现有众多分类
算法中，贝叶斯分类器由于其坚实的数学理论基础并能综合先验信息和数据样本信息，成为"-3前机器学习和数据挖
掘的研究热点之一。本文论述了内容图像检索中基于贝叶斯分类器的图像分类技术。介绍了贝叶斯分类器，叙述了
利用贝叶斯分类器进行图像分类的方法，以及图像特征的分布假定。最后通过对分类器的探讨，总结了贝叶斯估计
分类的不足。-The purpose of class is based on an existing image features to create a classifier able to predict the unknown image type. Many of the existing classification algorithm, the Bayesian classifier because of its solid mathematical theory-based and comprehensive information prior information and data samples, a " -3 before the machine learning and data mining research focus of this paper discusses the Content-based Image Retrieval Bayesian classifier image classification techniques. introduced Bayesian classifier, described the use of Bayesian classifier for image classification methods, and the distribution of image features is assumed finally by classifier discussion summarizes the classification of the lack of Bayesian estimation. Platform: |
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Author:刘东 |
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Description: 介绍数据挖掘的10种主要算法及其应用 一种透过数理模式来分析企业内储存的大量资料，以找出不同的客户或市场划分，分析出消费者喜好和行为的方法。 -Top 10 algorithms in data mining his paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5,k-Means, SVM,
Apriori, EM, PageRank, AdaBoost,kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community. With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
review current and further research on the algorithm. These 10 algorithms cover classification, Platform: |
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Author:andyzygg |
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Description: 聚类分析是数据挖掘研究领域中一个非常活跃的研究课题) 本文重点分析了高维度数据的自动子空间聚类算法
-Cluster analysis is data mining a very active field of research topic) This paper focuses on high-dimensional data subspace clustering algorithm automatically Platform: |
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Author:sdc |
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Description: ：聚类分析是数据挖掘研究领域中一个非常活跃的研究课题) 本文重点分析了高维度数据的自动子空间聚类算法-: Cluster analysis is data mining a very active field of research topic) This paper focuses on high-dimensional data subspace clustering algorithm automatically Platform: |
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Author:sdc |
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Description: In this era an emerging filed in the data mining is data stream mining. The current research technique of the data stream is classification which mainly focuses on concept drift data. In mining drift data with the single classifier is not sufficient for classifying the data. Because of the high dimensionality and does not get processed within considerable time, memory, false alarm rate is high, classification accuracy result is low. In this paper, proposed a Genetic based Intuitionistic fuzzy version of k-means has been introduced for grouping interdependent features. The proposed method achieves improvement in classification accuracy and perhaps to select the least number of features which show the way to simplification of learning task. The experimental shows that the advocated method performs well when compared with existing methods. Platform: |
Size: 107520 |
Author:Opencvresearcher |
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Description: 本文围绕入侵检测系统进行了深入细致的研究，介绍了入侵检测的研究进展，
分析了入侵检测系统的特征、结构和分类，分析了入侵检测系统的发展方向以及
在入侵检测中常用的数据挖掘方法，深入研究了聚类技术在入侵检测系统中的应
用，并对系统性能做出评估-This paper focuses on the intrusion detection system has been studied intensively, research progress intrusion detection,
Analysis of the characteristics, structure and classification of intrusion detection system, analyzes the development direction of intrusion detection systems and
Commonly used in intrusion detection data mining method, in-depth study of clustering technology in Intrusion Detection System
Use, and assess system performance Platform: |
Size: 993280 |
Author:路粮户 |
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Description: 聚类分析是数据挖掘研究领域中一个非常活跃的研究课题) 本文重点分析了高维度数据的自动子空间聚类算法-Cluster analysis is a data mining research area in a very active research topic) This paper focuses on automatic subspace clustering algorithm for high dimensional data Platform: |
Size: 6144 |
Author:汪静 |
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