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JAVA实现文本聚类，用到TF/IDF权重
- JAVA实现文本聚类，用到TF/IDF权重，用余弦夹角计算文本相似度，用k-means进行数据聚类等数学和统计 知识。,JAVA realization of text clustering, using TF/IDF weight, calculated using cosine angle between the text of similarity, using k-means clustering for data such as mathematical and statistical
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
julei
- TFIDF产生文本权重，在用K-means算法进行聚类。方法简单，可供相关人员参考继续深入学习-TFIDF generated text weights in with K-means clustering algorithm. The method is simple, the relevant officers for further study
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