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7下载：
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
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用JAVA语言实现的经典聚类算法k-means，聚类与分类不同，它是无监督的过程，,JAVA language used to achieve a classic clustering algorithm k-means, clustering and classification of different, it is the unsupervised process,
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用java实现的k means算法，搞过数据挖掘的人应该都知道的~-k means algorithm implement in java, the basic method of clustering in data mining
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java写的模糊c均值聚类算法和k均值聚类算法-fuzzy c means clustering algorithm and K means cluster in java
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改进的k-means方法，对聚类的实例节能型加权 少数类多数类的函数-Improved k-means method for clustering a small number of examples of energy-saving type of weighted majority of types of function
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it is java code for k means clustering algorithm
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k-means聚类算法的java代码实现-k-means clustering algorithm java code
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This code is K-means clustering code written in java.
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K means clustering of data implemented for all kinds of data-K means clustering of data implemented for all kinds of data...
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The package aims at providing an implementation of k-means Clustering Algorithm in Java. The package does not provide for any UI and it is up to the user to display the output in the required format.
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k-means算法的java描述，用java语言编写的k-means算法，用于聚类和分类-K-means algorithm java is described, using java language k-means algorithm, for clustering and classification
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Java 实现k-means 聚类算法，分别以迭代次数及分配不再发生变化为算法终止条件，用图片作为数据集，比较运行时间-Java implementation of k-means clustering algorithm, respectively, and the distribution of the number of iterations of the algorithm terminates no change in the conditions, with a picture (o
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Content based image retrieval in java using k-means clustering and haar wavelet transform
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k means clustering algorithm implementation in Java
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kmeans算法的Java实现。算法接受参数 k 然后将事先输入的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。-k means algorithm is implemented in Java. Receiving algorithm parameter k and n data objects entered beforehand into k clusters in order to satisfy such cluste
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