搜索资源列表
k-means1
- Python version of k-means for data clustering
DE
- Python version of Differential Evolution algorithm applied for clustering problem
ABC-clustering-with-cluster-representation
- Implementation of Artificial Bee Colony algorithm for data clustering with cluster representation and a global best neighborhood function
ABC-clustering-with-centroid-representation
- Implemantation of ABC algorithm for data clustering with centroid representation of solutions, and graphic representation of iris data set
Mahout算法解析与案例实战
- Mahout包含许多实现，包括聚类、分类、推荐过滤、频繁子项挖掘。此外，通过使用 Apache Hadoop 库，Mahout 可以有效地扩展到云中。(Mahout includes many implementations, including clustering, classification, recommendation filtering, frequent sub item mining. In addition, by using the Apache Hadoop librar
xarentheses
- kmeans算法实现 a simple k-means clustering routine returns the clus()
IABC_KMC_test_on_Iris_wine_glass
- 克服K均值聚类算法易受初始聚类中心影响的缺点，优化K均值聚类算法(The K mean clustering algorithm is easily affected by the initial cluster center, and the K mean clustering algorithm is optimized.)
基于聚类的细分研究
- 使用R语言进行聚类分析的例子，包括层次聚类，k均值聚类，密度聚类等(Examples of clustering analysis using R language, including hierarchical clustering, K mean clustering, density clustering, etc.)