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利用Java实现的K-均值算法,K-Mean 分群法是一种分割式分群方法,其主要目标是要在大量高纬的资料点中找出 具有代表性的资料点;这些资料点可以称为群中心,代表点;然后再根据这些群中心,进行后续的处理,可用于数据挖掘中的聚类分析-Java implementation using K-means algorithm, K-Mean grouping method is a fragmented grouping method, whose main goal is to a large nu
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本程序是一个用java语言开发的apriori算法的源代码,该算法主要用于数据挖掘。-This procedure is developed using java language apriori algorithm source code, the algorithm is mainly used for data mining.
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Implement FP-Growth algorithm of Assosiation Rule Data Mining using Java
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Insurance trade system using data mining algorithm , in this system we take a survey of insurance policy and present in bar chat-Insurance trade system using data mining algorithm , in this system we take a survey of insurance policy and present in b
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Mining the E-commerce Data to Analyze the Target Customer Behavior is developed in Java. It clusters customer segments by using K-Means algorithm and data from web log of various e-commerce websites. Consequently, the results showed that there was a
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基于将数据挖掘与并行技术结合,学习数据挖掘中关联规则算法,用java编写出最高效的apriori改进算法,用ubuntu上的eclipse作为开发平台,通过在eclipse上安装hadoop插件的方法建立并行平台。-Based on the data mining combined with parallel technology, learning algorithms in data mining association rules, using Java to write out the
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数据挖掘经典算法实现。使用这个算法分别对图片和DBLP上面学者的合作关系图进行了聚类,然后评估聚类的结果。算法实现用的java,DBLP的数据的搜集和预处理是用Python编写。-Classical data mining algorithm. Using this algorithm, respectively, pictures and cooperation between scholars DBLP above graph clustering, and then evaluate th
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数据挖掘中random foreast算法的实现,使用java实现的-Data Mining in random foreast algorithm, implemented using java
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数据挖掘中adaboost算法的实现,使用java实现的-Data Mining in adaboost algorithm, implemented using java
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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 algorith
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这是数据挖掘中的k均值聚类算法,用java语言编写的,对于搞聚类的人士很有帮助-This is the data mining k-means clustering algorithm, using java language, for persons engaged in clustering helpful
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