http://blog.csdn.net/pipisorry/article/details/49183379

海量数据挖掘Mining Massive Datasets(MMDs) -Jure Leskovec courses学习笔记之流算法Stream Algorithms

Stream Algorithms:  "Streams" are data inputs to a system that arrive at a very high rate, typically too fast to do anything significant with each arriving input.  Examples include data beamed down from a satellite, or click streams for
a popular Web site.  In this model, it is often necessary to accept a less-than-accurate answer to questions such as "how many different items have I seen at least once in this stream?"

这个没时间写，下次有空写吧╮(╯_╰)╭

皮皮blog

from:http://blog.csdn.net/pipisorry/article/details/49183379

海量数据挖掘MMDS week3:流算法Stream Algorithms的更多相关文章

  1. 海量数据挖掘MMDS week3:社交网络之社区检测：高级技巧

    http://blog.csdn.net/pipisorry/article/details/49052255 海量数据挖掘Mining Massive Datasets(MMDs) -Jure Le ...

  2. 海量数据挖掘MMDS week6: MapReduce算法（进阶）

    http://blog.csdn.net/pipisorry/article/details/49445519 海量数据挖掘Mining Massive Datasets(MMDs) -Jure Le ...

  3. 海量数据挖掘MMDS week3:社交网络之社区检测：基本技巧

    http://blog.csdn.net/pipisorry/article/details/49052057 海量数据挖掘Mining Massive Datasets(MMDs) -Jure Le ...

  4. 海量数据挖掘MMDS week2: 局部敏感哈希Locality-Sensitive Hashing, LSH

    http://blog.csdn.net/pipisorry/article/details/48858661 海量数据挖掘Mining Massive Datasets(MMDs) -Jure Le ...

  5. 海量数据挖掘MMDS week7: 局部敏感哈希LSH（进阶）

    http://blog.csdn.net/pipisorry/article/details/49686913 海量数据挖掘Mining Massive Datasets(MMDs) -Jure Le ...

  6. 海量数据挖掘MMDS week2: 频繁项集挖掘 Apriori算法的改进：非hash方法

    http://blog.csdn.net/pipisorry/article/details/48914067 海量数据挖掘Mining Massive Datasets(MMDs) -Jure Le ...

  7. 海量数据挖掘MMDS week2: 频繁项集挖掘 Apriori算法的改进：基于hash的方法

    http://blog.csdn.net/pipisorry/article/details/48901217 海量数据挖掘Mining Massive Datasets(MMDs) -Jure Le ...

  8. 海量数据挖掘MMDS week1: Link Analysis - PageRank

    http://blog.csdn.net/pipisorry/article/details/48579435 海量数据挖掘Mining Massive Datasets(MMDs) -Jure Le ...

  9. 海量数据挖掘MMDS week5: 聚类clustering

    http://blog.csdn.net/pipisorry/article/details/49427989 海量数据挖掘Mining Massive Datasets(MMDs) -Jure Le ...

随机推荐

  1. Lintcode393 Best Time to Buy and Sell Stock IV solution 题解

    [题目描述] Say you have an array for which the i th element is the price of a given stock on day i. Desi ...

  2. JavaScript正则表达式模式匹配(1)——基本字符匹配

    var pattern=/g..gle/; //点符号表示匹配除了换行符外的任意字符 var str='g78gle'; alert(pattern.test(str)); var pattern=/ ...

  3. Zookeeper和Chubby【分布式协调系统】

    前言(对于协调系统来说其客户端往往是分布式集群) 大规模分布式系统需要解决各种类型的协调需求: 当集群中有新的进程或服务器加入时,如何探测到它的加入?如何能够自动获取配置参数? 当配置信息被某个进程或 ...

  4. jQuery 遍历 – 后代

    后代是子.孙.曾孙等等. 通过 jQuery,您能够向下遍历 DOM 树,以查找元素的后代. 向下遍历 DOM 树 下面是两个用于向下遍历 DOM 树的 jQuery 方法: children() f ...

  5. Python 2.7 闭包的局限

    想法源自:http://stackoverflow.com/questions/141642/what-limitations-have-closures-in-python-compared-to- ...

  6. Angularjs + Bootstrap 制作的一个TODO List

    看了一个关于Angularjs的视频,视频内容讲解的是如何制作一款TODO list形式的SPA(Simple Page Application,单页面应用).为了增强理解,下面写了一篇文章,用以复习 ...

  7. Node.js 撸第一个Web应用

    使用Node.js 创建Web 应用与使用PHP/Java 语言创建Web应用略有不同. 使用PHP/Java 来编写后台代码时,需要Apache 或者 Nginx 的HTTP 服务器,而接受请求和提 ...

  8. RxJava（六） retryWhen操作符实现错误重试机制

    欢迎转载,转载请标明出处: http://blog.csdn.net/johnny901114/article/details/51539708 本文出自:[余志强的博客] 业务需求 当我们在app里 ...

  9. Spark-1.6.0中的Sort Based Shuffle源码解读

    从Spark-1.2.0开始,Spark的Shuffle由Hash Based Shuffle升级成了Sort Based Shuffle.即Spark.shuffle.manager从Hash换成了 ...

  10. 11 PopupMenu菜单和代码例子

    PopupMenu 弹出式菜单 API 11以上可用 1. 获取弹出菜单的对象 2. 在res里的menu添加菜单项 3. 将布局里的菜单项 给弹出菜单 4. 进行监听弹出菜单 5. 展示出弹出菜单 ...