Description: 说明：
1、里面有什么：
1.1、org.bouncycastle.*下的所有软件是bouncycastle组织开发的软件包
1.2、org.infosecurity.*下的软件包括：
DES_CBC算法的实现
RSA算法的实现（包括：密钥生成、公钥加密和私钥解密、数字签名及签名验证）
数字信封的实现
利用数字信封实现大文件的加密、解密。
2、有什么用：
2.1 研究、学习信息安全中的密码学设计与实现技术。
2.2 当然如果大家觉得在其它地方用的上，我会觉得很高兴的。
3、还有什么改进：
3.1 对文件加密、解密的时候要频繁的IO，如果加一个缓冲区，比如10K、50K等，
只有缓冲区满了才写入DISK这样将提高效率。
3.2 RSA私钥积存的时候可以按PKCS8形式，再经BASE64编码保存比较好。-Note : 1, mean there : 1.1, org.bouncycastle .* of all software development organizations is bouncycastle package 1.2, org.infosecurity .* The software includes : DES_CBC algorithm to achieve the realization of the RSA algorithm (including : key generation, public key and private key encryption decryption, digital signatures and signature verification), the realization of digital envelope using digital envelope realize document encryption, decryption. Two, what is the use of : 2.1, learning the information security cryptography design and technology. 2.2 Of course, if you think in the other parts, I feel very happy. Three, what is there for improvement : 3.1 pairs of file encryption, decryption, and the time to frequent the IO, plus a buffer, such as 10K, 50K, the only buffer is needed for Platform: |
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Author:雷敏 |
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Description: k-means聚类算法的java代码实现,良好的代码风格，适合扩充-k-means clustering algorithm to achieve the java code, the code of good style, suitable for expansion Platform: |
Size: 4096 |
Author: |
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Description: k-means是一种经典的聚类算法，这是用java实现k-means的源码，其中包括了测试数据文件-k-means is a classical clustering algorithm, which is used k-means realize java source code, including test data files Platform: |
Size: 4096 |
Author:尹哲 |
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Description: knn算法演示代码，可自动学习输入点，并形象表现输入点的最近k个邻居。-KNN algorithm demo code can be automatically input to learn, and the image input of the recent performance of k-neighbors. Platform: |
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Author: |
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Description: 数据挖掘算法。K-Means聚类数据挖掘算法。该算法是用Java语言编写的。-Data mining algorithms. K-Means clustering algorithm for data mining. The algorithm is a Java language. Platform: |
Size: 41984 |
Author:张志娟 |
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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: 这是K-neans动态聚类算法的源程序，是人工智能领域很有用的一种聚类方法。-This is K-neans source dynamic clustering algorithm, the field of artificial intelligence are useful in a clustering method. Platform: |
Size: 29696 |
Author:zhaohuiqin |
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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: |
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Description: 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 number of data points in the high latitudes to find representative data points these data points can be called cluster center, the representative point then these groups center, follow-up treatment can be used for data mining in the cluster analysis Platform: |
Size: 2048 |
Author:阚涛 |
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Description: K-mean Algorithm
Initialisation: set seed points
Assign each object to the cluster with the nearest seed point
Compute seed points as the centroids of the clusters of the current partition (the centroid is the centre, i.e., mean point, of the cluster)
Go back to Step 1), stop when no more new assignment
GUI in java Best Proj Devide web log in various clusters- K-mean Algorithm
Initialisation: set seed points
Assign each object to the cluster with the nearest seed point
Compute seed points as the centroids of the clusters of the current partition (the centroid is the centre, i.e., mean point, of the cluster)
Go back to Step 1), stop when no more new assignment
GUI in java Best Proj Devide web log in various clusters Platform: |
Size: 38912 |
Author: |
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Search - K Mean algorithm in java