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BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamic
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本程序可以实现图像k均值聚类在数据挖掘中的相关算法。计算出图像的直方图-This program can image k means clustering in data mining correlation algorithm. Calculate the histogram
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数据挖掘 图像聚类处理
最终结果是每个簇生成一张图片-Image clustering data mining process to generate the final result is a picture of each cluster
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K-means算法是最为经典的基于划分的聚类方法，是十大经典数据挖掘算法之一。K-means算法的基本思想是：以空间中k个点为中心进行聚类，对最靠近他们的对象归类。通过迭代的方法，逐次更新各聚类中心的值，直至得到最好的聚类结果。-K-means algorithm is based on the division of the classic clustering method, is ten classic one of data mining algorithm. K-means the
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变色龙算法,很经典的一个算法,适合于研究数据挖掘的朋友下载,用于聚类研究.-Chameleon algorithm, a classic algorithm for data mining research of friends to download, for clustering.
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It is a java implementation of Expectation Maximization algorithm that can be used for clustering objects in data mining
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数据挖掘Kmeans算法C/C++语言实现。
提供接口让使用者可以指定输入输出文件、聚类结果中类别个数、中心点计算方式、初始化方法。-Kmeans,data mining algorithms,C/C++language.
Provides the interface so that users can specify the input and output file, type in the number of clustering results, the center of ca
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模糊C均值聚类算法实现数据挖掘的matlab源代码-Fuzzy C-Means clustering algorithm for data mining matlab source code
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DBSCAN algorithm in clustering and data mining
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数据挖掘和数据处理的注意点和方法，专业术语熵，聚类等的算法说明-Algorithm descr iption of the data mining and data processing attention points and methods, terminology, entropy, clustering, etc.
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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
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空间聚类算法GDBSSCAN 对数据挖掘有用
-Spatial clustering algorithm GDBSSCAN useful data mining
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基于模型的时间序列数据挖掘---聚类和预测相关问题研究
---- Clustering and forecasting issues related to model-based time-series data mining
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主要介绍在大型数据库中发现知识（Knowledge Discovery in Large Databases, KDD）的各种技术，是专门针对决策支持中的各类问题进行讨论的高端课程。面向对象为软件工程专业硕士研究生。
本课程讲授的主要内容包括：数据预处理、数据仓库及OLAP、概念描述型数据挖掘、关联规则挖掘、分类挖掘和预测以及聚类挖掘，涉及的领域包括数理统计、概率论、机器学习、信息论、集合论等等。-Introduces knowledge discovery in large dat
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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
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：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
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k-means数据挖掘算法，基于kmeans聚类算法工艺参数基准值的挖掘-mining data mining of the k-means algorithm, based on the kmeans clustering algorithms process parameters reference value
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KMEAN C#
In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data sp
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MDR,数据挖掘，GWAS，分类算法，聚类算法，GWAS的应用-MDR, data mining, GWAS classification algorithm, clustering algorithm, GWAS applications
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k_means聚类算法 实现图像分割 数据挖掘-k_means clustering algorithm to achieve image segmentation data mining
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