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[Documents] 属性均值聚类二叉树及其在人脸识别中的应用 DL : 0: 采用二叉树数据结构，属性均值聚类算法在图象识别中的应用。-using a binary tree data structure, attributes means clustering algorithm in image recognition applications.
Date : 2025-12-20 Size : 105kb User : 石支柱
[Documents] 399 基于聚类分析的属性数据挖掘技术 DL : 0: 数据库中的数据都有各种属性，目前算法很少涉及这部分，这是一个关于属性的聚类算法。-the data in the database have different attributes, the current algorithm is rarely associated with this part, it is an attribute of the clustering algorithm.
Date : 2025-12-20 Size : 25kb User : 李中
[Documents] Birch DL : 0: Birch算法的c代码,做数据聚类-Birch algorithm c code to do data clustering
Date : 2025-12-20 Size : 251kb User :
[Documents] k-means DL : 0: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.-Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Date : 2025-12-20 Size : 1kb User : nadjia
[Documents] k-junzhi DL : 0: 通过对K-均值算法的编程实现，加强对该算法的理解和认识。提高自身的知识水平和编程能力，认识模式识别在生活中的应用。算法思想K-均值算法的主要思想是先在需要分类的数据中寻找K组数据作为初始聚类中心，然后计算其他数据距离这三个聚类中心的距离，将数据归入与其距离最近的聚类中心，之后再对这K个聚类的数据计算均值，作为新的聚类中心，继续以上步骤，直到新的聚类中心与上一次的聚类中心值相等时结束算法。-By programming K- means algorithm implementation, strengthen understanding and awareness of the algorithm. Improve their level of knowledge and programming skills, understanding of pattern recognition in life. The main idea thought K- means algorithm is the first in the classification of the data needed to find K sets of data as the initial cluster centers, and other data to calculate the distance the cluster center of the three, the data included in its closest poly class center, after which the K clustering of these data to calculate the mean, as a new cluster centers, continue the above steps until the new cluster centers and cluster centers on a value equal to the end of the algorithm.
Date : 2025-12-20 Size : 12kb User : gh
[Documents] FCM DL : 0: 模糊聚类算法源码，通过迭代聚类中心，以及隶属度函数，完成代码运算，用于数据挖掘初学者使用。(Fuzzy clustering algorithm source code, through the iterative clustering centers and membership function, complete code for data mining operations, for beginners to use.)
Date : 2025-12-20 Size : 2kb User : 烟花易冷
[Documents] 面向高维数据的子空间聚类算法研究 DL : 0: 面向高维数据的子空间聚类算法研究，包括所有的硬子空间，软子空间等聚类算法，也包括一些新提出的子空间聚类算法及其伪代码和实验分析。(The research of subspace clustering algorithms for high-dimensional data includes all the hard subspace, soft subspace and other clustering algorithms. It also includes some newly proposed subspace clustering algorithms and their pseudo code and experimental analysis.)
Date : 2025-12-20 Size : 2.88mb User : 枫歌
[Documents] Introduction.To.Data.Mining].Pang DL : 0: 数据挖掘介绍，包括数据预处理，可视化，预测建模，关联分析，聚类和异常检测(Data mining introduces data preprocessing, visualization, predictive modeling, association analysis, clustering, and anomaly detection)
Date : 2025-12-20 Size : 47.52mb User : 张云纯
[Documents] 文本分析聚类实战 DL : 0: 文本挖掘是从大量的文本数据中抽取隐含的，求和的，可能有用的信息。通过文本挖掘实现 ?Associate：关联分析，根据同时出现的频率找出关联规则 ?Cluster：将相似的文档（词条）进行聚类 ?Categorize：将文本划分到预先定义的类别里(Text mining is a kind of information that is extracted from a large number of text data, which may be useful. Implementation of text mining 1. Associate: association analysis, finding association rules based on the frequency of simultaneous occurrence 2.Cluster: clustering similar documents (entries) 3.Categorize: dividing text into a predefined category)
Date : 2025-12-20 Size : 277kb User : IS-Rookie