搜索资源列表
文本分析聚类实战
- 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. Implementa
代码
- 先用的层次分析法筛选变量,而后使用聚类分析中的kmeans和pam两种方法,优点在于可以快速聚类,针对较大的数据量(clustering methodology)
代码
- 使用的是深圳市数学建模的电信用户数据,基于数据筛选出高价值用户,并分析他们的消费特点。代码中包括了层次分析法,聚类分析,数据的缺失值、异常值的处理(The code includes the analytic hierarchy process, the clustering analysis, the missing values of the data, and the handling of the abnormity values)
textclustering-master
- 对于大文本进行挖掘聚类,该方法不考虑文字词语出现的频率信息,考虑上下文语境,将所有的字根据预定义的特征进行词位特征学习,获得一个训练模型。然后对待分字符串的每一个字进行词位标注,最后根据词位定义获得最终的分词结果。(Digging for large text clustering, the method does not consider the text word frequency of information, considering the context, all the words
Dbscan
- Dbscan聚类源代码。 可以实现对多维数据进行聚类。(Dbscan cluster source code. The multidimensional data can be clustered.)
algorithm in paper
- science平台代码源文件,主要讲的是一种基于密度的聚类方法,(Clustering by fast search and find of density peaks)
Cluster analysis
- 对输入的数值进行聚类分析,分析阈值可自己设置,聚类方法可以分为欧氏距离和夹角余弦两种(The value of the input is analyzed by cluster analysis, and the threshold can be set by itself. The clustering method can be divided into two kinds: Euclidean distance and angle cosine.)
FCM
- 模糊C均值(FCM)聚类算法分割图像,该算法可以进行图像的分割(Fuzzy C-Means (FCM) clustering algorithm to segment images, which can be used for image segmentation)
r2
- 简单kmeans聚类,三个实例可以对比,有数据有代码有解释步骤(kmeans, there are three samples as well as data and explanation.)
Kmeans
- python实现的k-means聚类算法(k-means clustering algorithm implemented by python)
AP算法代码
- ap聚类算法的MATLAB实现代码,包含了解析与源代码,供学习参考用(MATLAB implementation code of AP clustering algorithm)
蚁群聚类
- 基于蚁群算法的K-means聚类分析模型(Ant colony clustering analysis model)
模糊c均值聚类算法
- 模糊c均值聚类算法的实现,还有相对的测试数据集(Fuzzy C-Means clustering algorithm)
python实现代码、测试数据集及结果
- 密度距离矩阵优化聚类算法python实现(Python implementation of density distance matrix optimization clustering algorithm)
K---MEANS
- 随机生成1000个二维坐标点并用K-means算法计算聚类结果(1000 two-dimensional coordinate points are generated randomly and the clustering results are calculated by K-means algorithm)
FCM
- 使用模糊C均值聚类(FCM)的方法对状态进行分类,其优点首先是可以根据实际情况自动确定聚类中心,减少人工干涉的因素,其次,对状态特征参数不是进行硬分类,而是通过隶属度的表征方式对其聚类,更加符合现实状态类别之间不具备明显界限的实际问题。(The use of fuzzy C mean clustering (FCM) method to classify the state, its advantage is first can automatically determine the clust
kmeans
- kmeans聚类,多数据检索分类,处理原始数据(kmeans,Multi - data retrieval classification, processing raw data.)
matlab编写的EM聚类算法
- em聚类算法,比较基础的算法,可自行改进(em clustering algorithm, more basic algorithm, self-improvement)
yiqunjulei
- 采用基本蚁群算法进行无线传感网络节点的聚类仿真(Ant Colony Algorithm Clustering Simulation)
EWKM
- 子空间聚类算法EWKM (Entropy Weighting K-Means) 在matlab上的实现。(Entropy Weighting K-means which is one of the subspace clustering algorithm written in Matlab.)