Description: K-MEANS算法
输入：聚类个数k，以及包含 n个数据对象的数据库。
输出：满足方差最小标准的k个聚类。
处理流程：
（1） 从 n个数据对象任意选择 k 个对象作为初始聚类中心；
（2） 循环（3）到（4）直到每个聚类不再发生变化为止
（3） 根据每个聚类对象的均值（中心对象），计算每个对象与这些中心对象的距离；并根据最小距离重新对相应对象进行划分；
（4） 重新计算每个（有变化）聚类的均值（中心对象）-K-MEANS algorithm Input: cluster number k, and contains n data object database. Output: the minimum standards to meet the variance k-clustering. Deal flow: (1) a data object from the n choose k object as initial cluster centers (2) cycle (3) to (4) until a change in each cluster is no longer so far (3) according to each Clustering objects mean (central object), calculated for each object with these centers to object distance and in accordance with a minimum distance between a re-division of the corresponding object (4) re-calculated for each (change) clustering of the mean (central object ) Platform: |
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Author:快快 |
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Description: java实现的K-means聚类算法，结合swing，带有界面功能，操作简单直观-java implementation of K-means clustering algorithm, combined with the swing, with interface features simple, intuitive operation Platform: |
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Description: this code of clustering with k-means algorithm with java-this is code of clustering with k-means algorithm with java Platform: |
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Description: 用VC或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（自己选择）作为数据集，比较运行时间（画出时间与像素点的关系曲线图，因此须用多幅像素个数不同的图片进行实验），提交实验报告与源代码。-K-means clustering algorithm for algorithm termination conditions, with a picture (their choice) as a data set to compare the running time (draw time and the relationship of the pixels, respectively, the number of iterations and distribution no longer changes with VC or Java experimental curve, so to use pictures of different pieces of the number of pixels), submitted to the lab report with source code. Platform: |
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Author:郭跃龙 |
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Description: 数据挖掘kmeans图像聚类实验报告
用 VC 或 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（自己选择）作为数据集，比较运行时间（画出时间与像素点的关系曲线图，因此须用多幅像素个数不同的图片进行实验） 提交实验报告与源代码。 -Data mining to achieve the k-means clustering algorithm the kmeans image clustering experiment report with VC or Java, respectively, to the number of iterations and distribution no longer change the termination condition for the algorithm as a data set with a picture (of their choice), compare running time (painting out time and the graph showing the relationship of the pixel point, and is therefore subject to experiment with multiple number of pixels of different pictures) submitted to the lab report with the source code. Platform: |
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Author:周生勇 |
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Description: 数据挖掘kmeans图像聚类实验,用 VC 或 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（自己选择）作为数据集，比较运行时间（画出时间与像素点的关系曲线图，因此须用多幅像素个数不同的图片进行实验） 提交实验报告与源代码-Data mining kmeans image clustering experiments, using VC or 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 (of your choice) as the data set to compare the running time (painting graph of the relationship between time and the pixel is therefore subject to the number of pixels to experiment with different pieces of the picture) to submit test reports and source code Platform: |
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Description: 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 (of your choice) as the data set to compare the running time Platform: |
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Author:郑鹏 |
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Description: 利用java实现了k-means聚类，数据集是由Math.random函数产生的。-K-means clustering was achieved with Java.The data set was created by Math.random. Platform: |
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Author:李洪成 |
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Description: java写的k-means，随机选择聚类中心-the realizationg of K-means clustering algotithm based on Java,with random selection of clustering centers Platform: |
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Author:zhangsun |
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Description: 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 of the two documents, and achieve the text clustering with the standard k-means algorithm. Source code for Java implementation) Platform: |
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