搜索资源列表
K-Means
- K_Means(java)算法的实现,有界面,灵活性强,交互性强。-K_Means (java) algorithm, there are interfaces, flexibility, and strong interaction.
kmeans
- java k均值源码,实现了k-means的算法,并给出界面显示。实例中通过二维空间中的点进行聚类。-java k-means algorithm, display the cluster result on the two demension.
k-means_Program
- 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 to accept input k then n-k of data objects into a cluster in order to make the cluster available to meet: t
TextClustering
- 文本聚类算法包含 tfidf的实现 k-means算法的实现-Text clustering algorithm contains tfidf implementation of the k-means algorithm to achieve
textcluster
- java版的k-means算法,实现文本聚类功能-the k-means algorithm in java
Kmeans
- 算法思想:提取文档的TF/IDF权重,然后用余弦定理计算两个多维向量的距离来计算两篇文档的相似度,用标准的k-means算法就可以实现文本聚类。源码为java实现(Algorithm idea: extract the TF/IDF weight of the document, then calculate the distance between two multidimensional vectors by cosine theorem, calculate the similarity