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Kmeans
- 自适应K-均值聚类算法,能够随着聚簇数目的变化而自动调整聚类数,以最合适的聚簇数目来进行数据分类。-Adaptive K-means clustering algorithm, the number can be clustered with the changes in the number of clusters is automatically adjusted to the most appropriate number for clustered data classification
KMeans
- K-均值聚类算法,属于无监督机器学习算法,发现给定数据集的k个簇的算法。 首先,随机确定k个初始点作为质心,然后将数据集中的每个点分配到一个簇中,为每个点找距其最近的质心, 将其分配给该质心对应的簇,更新每一个簇的质心,直到质心不在变化。 K-均值聚类算法一个优点是k是用户自定义的参数,用户并不知道是否好,与此同时,K-均值算法收敛但是聚类效果差, 由于算法收敛到了局部最小值,而非全局最小值。 K-均值聚类算法的一个变形是二分K-均值聚类算法,该算法首先将所有点作为一个簇,然
Kmeans
- k-means 算法接受输入量 k ;然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足:同一聚类中的对象相似度较高;而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”(引力中心)来进行计算的。-k-means algorithm accepts input k then n data objects into k clusters in order to make clustering satisfy obtained: the objects i
kmeans
- matlab实现的k均值聚类算法,能对不同维度的数据进行聚类,有测试程序,欢迎下载-k-means clustering algorithm matlab implementation, capable of clustering data in different dimensions, with a test program, welcome to download
k_means
- 经典的k均值聚类算法,用于无监督学习和数据分类,广泛应用于数据处理和模式识别领域-Classical k-means clustering algorithm for unsupervised learning and data classification, widely used in data processing and pattern recognition
KMeans
- K-means算法的实现,通过对没有标记的原始数据进行kmeans聚类得到的分类。-K-means algorithm to achieve
kMeans
- 机器学习算法,无监督学习,利用k均值聚类算法对未标注数据分组-Machine learning algorithms, unsupervised learning, the use of k-means clustering algorithm for unlabeled data packets
Kmeans
- K-means K均值聚类算法,不是用工具箱编的,对随机产生的数据进行聚类。压缩文件包括m函数、包含主程序和子函数的word文档。-K-means clustering algorithm, not with the toolbox series of randomly generated data clustering.M functions including compressed files, containing the main program and subroutines word
kmeans
- 在数据挖掘课堂中 老师要实现的k均值算法 做一些简单的点的分类-In data mining classroom teacher to achieve k-means algorithm to do some simple points classification
CPP
- 基于K-均值聚类算法的数据分类方法C++实现-K-means c++
Matlab
- 邓勇夫 基于K-均值聚类算法的数据分类方法MATLAB实现-K-means MATLAB
DataMiningCluster-master
- 数据挖掘的聚类算法实现 Implementation of text clustering algorithms including K-means, MBSAS, DBSCAN-data mining cluster
a4da35a45805
- 动态聚类的k-means图:正确的程序分出的输入数据-K-means clustering dynamic figure: the correct procedures to separate input data
Kmeans
- 利用java实现了k-means聚类,数据集是由Math.random函数产生的。-K-means clustering was achieved with Java.The data set was created by Math.random.
Kmeans
- 本程序是一个K-means聚类算法程序,自己生成4堆数据,然后让算法自己聚类-This program is a K-means clustering algorithm procedures, generate their own four heap data, then let yourself clustering algorithm
kmeans
- k均值聚类方法。 在给定一个有n个对象的数据集,划分聚类技术将构造数据进行k个划分,每一个划分代表一个簇,k小于等于n。-k-means clustering method. Given a set of n objects data, dividing the data clustering techniques to construct k partitions, each partition represents a cluster, k less than or equal n.
Votingkmeans
- 基于文本数据的投票k-means聚类融合算法的实现-Voting k-means clustering text-based data fusion algorithm implementation
kmeans
- k-means算法是文本聚类经典算法,也是数据挖掘十大经典算法之一。k-means算法Java实现。-k-means algorithm is a classical algorithm text clustering, data mining is one of the ten classic algorithms. k-means algorithm is implemented in Java.
k_means
- k-means聚类分析matlab实现,有详细注释和测试数据-k-means clustering matlab realization, detailed notes and test data
Kmeans2_k2
- 对给定的一组数据点采用k均值进行划分,并用散点图表示了出来-A set of data points in a given using k-means partition, and a scatter diagram