Description: 经典cure聚类算法,实现用c++语言.大家-classic cure clustering algorithm, and using the c language. We look at Platform: |
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Author:孔明 |
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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 |
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Description: leach用java编写的源代码，根据簇头剩余能量与初始能量比较，判断是否重新选择簇头。-The source code written in Java leach, according to initial cluster head the surplus energy and energy comparison, determine whether chose to cluster head.
Platform: |
Size: 13312 |
Author:lixiaoyan |
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Description: In this text, students of applied mathematics, science and engineering are introduced to fundamental ways of thinking about the broad context of parallelism. The authors begin by giving the reader a deeper understanding of the issues through a general examination of timing, data dependencies, and communication. These ideas are implemented with respect to shared memory, parallel and vector processing, and distributed memory cluster computing. Threads, OpenMP, and MPI are covered, along with code examples in Fortran, C, and Java. The principles of parallel computation are applied throughout as the authors cover traditional topics in a first course in scientific computing. Building on the fundamentals of floating point representation and numerical error, a thorough treatment of numerical linear algebra and eigenvector/eigenvalue problems is provided. By studying how these algorithms parallelize, the reader is able to explore parallelism inherent in other computations, such as Monte Carlo methods Platform: |
Size: 1644544 |
Author:zahid |
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Description: weka平台的文本分类测试，源代码为java-Text categorization test weka platform, the source code for the java Platform: |
Size: 277504 |
Author:ziyan |
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Description: java代码，sql数据库,功能完善，欢迎下载基于 java / jsp 技术构建的博客系统，不同于asp、php语言的博客系统，blog4j采用三层架构（展现层、业务层、数据层）、基于WEB 2.0相关技术、且支持分布式集群化部署，有着易扩展、高性能等特性，是中大型博客网站的理想博客系统解决方案。(Java code, SQL database, perfect function, welcome to download the blog system based on Java / JSP technology, different from the ASP, PHP language blog system, blog4j adopts three tier architecture (display layer, business layer, data layer), WEB 2 related technology, and support distributed cluster deployment, and has easy to expand and high sex It's an ideal blog system solution for medium and large Blog websites) Platform: |
Size: 10170368 |
Author:小伟伟1 |
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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: |
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Author:648577896 |
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Search - Cluster Java Code