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hadoop-0.1.0.tar
- Hadoop是一个用于运行应用程序在大型集群的廉价硬件设备上的框架。Hadoop为应用程序透明的提供了一组稳定/可靠的接口和数据运动。在 Hadoop中实现了Google的MapReduce算法，它能够把应用程序分割成许多很小的工作单元，每个单元可以在任何集群节点上执行或重复执行。此外，Hadoop还提供一个分布式文件系统用来在各个计算节点上存储数据，并提供了对数据读写的高吞吐率。由于应用了map/reduce和分布式文件系统使得Hadoop框架具有高容错性，它会自动处理失败节点。已经在具有60
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
dbscan
- Density Based Spatial Clustering of Applications of Noise Uses a density-based notion of clusters to discover clusters of arbitrary shapes, in spatial databases Key idea: for each object of a cluster, the neighborhood of a given radius contains
mapreduce
- MapReduce: Simplied Data Processing on Large Clusters
Clustering
- NetBeans Project that clusters documents according to similarity calculated by dice coefficient.
Java-Cluster
- 基于JVM的Java应用集群解决方案。 -JVM' s Java Application Clusters based solutions. JVM' s Java Application Clusters based solutions.
Mining-e-Coomerce-data-to-analyze-customer-behavi
- 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
cluster
- J2EE集群相关内容，对J2EE中集群的概念作了全面的介绍，并提供J2EE集群实现机制的说明-J2EE cluster related content, the concept of cluster of J2EE provides a comprehensive introduction and provides the mechanism that J2EE Clusters
mahy
- 基于相对密度的聚类算法（DBSCAN算法），用于处理高密度簇完全被相连的低密度簇所包含的问题-Clustering algorithm based on relative density (DBSCAN algorithm), to handle high-density clusters are completely connected to the problem of low-density cluster contains
clusterlib-src-0.1.1
- clusterlib a goog implementation of clusters
chameleon
- JAVA实现变色龙chameleon算法，CHAMELEON是一种两阶段聚类法。第一阶段把点分成很多小的簇；第二阶段根据相近程度合并这些小的簇。-JAVA realization a chameleon chameleon algorithm, CHAMELEON clustering method is a two-stage. The first phase of the point divided into many small clusters these small clusters
KMEANS
- 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）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；（3）重新计算每个（有变化）聚类的均值（中心对象） （4）循环（2）到（3）直到每个聚类不再发生变化为止-Input: number of clusters k, and n data object contains a
clusters
- This application demonstrates the usage of the cluster validity measures
Kmeans
- Kmeans算法，用JAVA实现了3个簇和6个簇的分类。代码简单易懂-Kmeans algorithm, using JAVA realize the three clusters and clusters of six categories. Code is easy to understand
K_Means
- k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作为标准测度函数. k个聚类具有以下特点：各聚类本身尽可能的紧凑，而各聚类之间尽可能的分开。下面给出我写的源代码。-work process k-means al
kmeans
- 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
CanopyExm
- 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
WeightManager8
- 体重合理规划小软件是一款非常方便实用的体重记录与跟踪工具，使用本工具，你可以方便快捷的管理你的体重数据。-Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel
KMeans2
- 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
cureoptimal
- 改源码提供了一种能够自动检测应分类的数目的Cure算法-An updated Cure algorithm that can automatically detect the number of the clusters