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k-means聚类算法的java代码实现,良好的代码风格，适合扩充-k-means clustering algorithm to achieve the java code, the code of good style, suitable for expansion
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数据挖掘算法。K-Means聚类数据挖掘算法。该算法是用Java语言编写的。
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java的k-means聚类算法实现，使用了2维的聚类算法，在数据统计以及图像识别方面不错-java of the k-means clustering algorithm
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k means clustering algorithm
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简单易懂的k-means聚类算法，可运行！有详细注释说明。-Straightforward k-means clustering algorithm, run! Detailed explanatory notes.
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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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PHP Class to do K-Means clustering.
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使用k均值对用户进行聚类，有很强的实用性。-Using k-means clustering, and practicality.
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K-均值聚类算法，是一种随机选取数个数据中心进行点聚类处理进而生成分类的数据挖掘算法，具有很好的学习功能。-K-means clustering algorithm is a randomly selected number of data center point clustering process thereby generating classification data mining algorithms, with good learning function.
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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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k均值聚类算法代码, k均值聚类算法代码-k-means clustering algorithm code, k-means clustering algorithm code
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K-means聚类算法 用于图像处理 JAVA语言编写，聚类中值算法可运行-k-means clustering algorithm image processing
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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 is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the clu
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Canopy聚类算法是一个将对象分组到类的简单、快速、精确地方法。每个对象用多维特征空间里的一个点来表示。这个算法使用一个快速近似距离度量和两个距离阈值 T1>T2来处理。 Canopy聚类算法能快速找出应该选择多少个簇，同时找到簇的中心，这样可以大大优化 K均值聚类算法的效率 。-Canopy is a clustering algorithm to group objects into simple categories, fast, accurate method. Each obj
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机器学习的范畴，包括SVMs (based on libsvm), k-NN, random forests, decision trees。可以对任意的数据操作-Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature sel
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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
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K-means算法是硬聚类算法，是典型的基于原型的目标函数聚类方法的代表，它是数据点到原型的某种距离作为优化的目标函数，利用函数求极值的方法得到迭代运算的调整规则。-K-means clustering algorithm is hard, is a typical prototype-based clustering method on behalf of the objective function, it is a method of data points to a certain di
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用java语言实现k均值聚类的代码demo，可直接运行,无需调试。-Using java language k-means clustering code demo, it can be run directly without debugging.
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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
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