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: |
Size: 3072 |
Author:快快 |
Hits:
Description: weka全名是怀卡托智能分析环境（Waikato Environment for Knowledge Analysis），是一个公开的数据挖掘工作平台，集合了大量能承担数据挖掘任务的机器学习算法，包括对数据进行预处理，分类，回归、聚类、关联规则以及在新的交互式界面上的可视化-full name is weka intelligent analysis environment Waikato (Waikato Environment for Knowledge Analysis), is an open platform for data mining work, collection of a large number of data mining capable of undertaking the task of machine learning algorithms, including data pre-processing, classification, regression , clustering, association rules, as well as in the new interactive visualization interface Platform: |
Size: 10288128 |
Author:朱磊 |
Hits:
Description: 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 Platform: |
Size: 9216 |
Author:zhangwen0927 |
Hits:
Description: 聚类是把一组个体按照相似性归成若干类别，即“物以类聚”。它的目的是使得属于同
一类别的个体之间的距离尽可能的小而不同类别上的个体间的距离尽可能的大。聚类方
法包括统计方法、机器学习方法、神经网络方法和面向数据库的方法。
-Clustering is a group of individuals as to the similarity in accordance with a number of categories, that is, " 物以类聚." Its purpose is to allow individuals fall into the same category as far as possible the distance between the different categories of small and the individual as much as possible the distance between the major. Clustering methods, including statistical methods, machine learning, neural network methods and database-oriented approach. Platform: |
Size: 1422336 |
Author:qingpeng yu |
Hits:
Description: 基于局部搜索能力强、收敛速度快的特点,首先初始化一个没有子种群的全局种群,再在全局种群中采用迭代搜索,并对其中的个体进行聚类,当聚类簇中的个体数目达到规定的最小规模时形成一个子种群,然后在各子种群中进行迭代搜索并重新进行聚类,从而提高进化过程中种群的多样性,增强算法跳出局部最优的能力.该算法基于weka，用于weka拓展功能，需要 weka算法包支持。-Based on the local search ability, the characteristics of fast convergence, first initialize a sub-population of the overall population, then the overall population in the iterative search, and clustering of the individuals, when the clustering of individual cluster achieve the required minimum number of the scale of the formation of a subset of the population, and then in the sub-populations in the iterative search and re-clustering to improve the evolutionary process of population diversity, enhancement algorithm' s ability to jump out of local optimum. Platform: |
Size: 5120 |
Author:zhangrui |
Hits:
Description: 文档分类，用K均值，加入了K的选择算法，不用人为设定聚类个数-Document classification, using K-means, joined the K of the selection algorithm, not the number of artificial clustering Platform: |
Size: 20480 |
Author:王 |
Hits:
Description: 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 at least a minimum number of data-objects. (i.e. the density of each cluster must exceed a threshold value)
Choosing the distance function is the critical parameter.
An object that appears to be part of Noise at present, might, at a later stage, be included into one of the clusters.
Platform: |
Size: 4096 |
Author:nandish.hebbal |
Hits:
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: |
Size: 4153344 |
Author:吴娟 |
Hits:
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: |
Size: 5145600 |
Author:郑鹏 |
Hits:
Description: CLIQUE（Clustering In QUEst)是一种简单的基于网格的聚类方法，用于发现子空间中基于密度的簇。CLIQUE把每个维划分成不重叠的区间，从而把数据对象的整个嵌入空间划分成单元。它使用一个密度阈值识别稠密单元和稀疏单元。一个单元是稠密的，如果映射到它的对象数超过该密度阈值。(CLIQUE (Clustering In QUEst) is a simple grid based clustering method for the discovery of clusters based on density in subspace. CLIQUE divides each dimension into a non overlapping interval, dividing the entire embedded space of the data object into a unit. It uses a density threshold to identify dense and sparse units. A unit is dense, if the number of objects mapped to it is more than the density threshold.) Platform: |
Size: 15360 |
Author:景_天
|
Hits:
Description: # How To Run The Code ?
After downloading it to local,
1. cd to the folder of src/boliu/dbscansd/
2. compile all the .java files using:
javac *.java
3. cd to the folder of src/
4. execute the program using the following either command:
java boliu.dbscansd.Main inputfile outputfile lineNum eps minPts maxSpd maxDir isStop
* @param inputfile the input file path
* @param outputfile the output file path
* @param lineNum the designated number of trajectory points for clustering (if the size of the input file is less than lineNum, it will extract all the points)
* @param eps 1st parameter of DBSCANSD, the radius
* @param minPts 2nd parameter of DBSCANSD, the minimum number of points
* @param maxSpd 3rd parameter of DBSCANSD, the maximum SOG difference
* @param maxDir 4th parameter of DBSCANSD, the maximum COG difference
* @param isStop boolean value (0/1), if you would like to cluster stopping points (1) or moving points (0)
--e.g. java boliu.dbscansd.Main toy_data.csv output 70000 0.03 50 2 2.5 0
In this way, the program will do the job on toy_data.csv file.
It will extract the first 70,000 moving points from the data and then run DBSCANSD on the dataset.
The final output will be two files:
output_gv.csv (gravity vectors)
output_movingclusters.csv (original clustering results with more rows).
5. waiting for the result :) The running time will vary with different sizes of the input data and other input parameters.
6. Star it if it helps \*-\* Platform: |
Size: 11994582 |
Author:648577896 |
Hits:
Search - number clustering in java