标签：: Algorithms
Network Mining Based On Co-occurrence
Generate network based on co-occurrence was proposed several decades ago, however, it still occupies most of papers talking about network discovering. Here I want to give a simple introduction for network capture by conventional co-occurrence methods. The following contents are slightly related with the basis of my current work, they are out of date but still useful sometimes.
Cantor Expansion With Duplicate Elements
Cantor Expansion is simple, however, for permutations with duplicate elements, some factors are changed.For why I use English: Since I am going to write an English paper, I want to write several articles in English here for practice. Please forgive me for any inconvenience I may have caused to you.
复杂网络传统社区发现算法概述
复杂网络是复杂系统的抽象，其中一个重要特征是网络中所呈现出的社区结构。许多网络是异构的，对于构成网络的不同类型节点所组成的子图称为网络中的社区。整理了几个传统的社区发现算法流程和大致原理，记录备忘。
机器学习笔记（Chapter 14 - SVD简化）
从数据中提取一些关键信息可以使用奇异值分解（Singular Value Decomposition，SVD），可以简化数据，去除噪声，将数据映射到低维空间。
机器学习笔记（Chapter 12 - FP-growth算法）
FP-growth算法基于Apriori构建，先将数据集存储在FP树内，再发现频繁项集，速度通常快于Apriori两个数量级以上。FP-growth只需要对数据库扫描两次，而Apriori需要对每个潜在的频繁项集扫描一次数据集。Apriori算法拓展性更好，可以用于并行计算。
机器学习笔记（Chapter 11 - Apriori算法）
商店通过会员卡等忠诚度计划，可以获取顾客所购买商品的组合信息，从而更好地安排商品定价、市场促销等。从大规模数据集中寻找物品间的隐含关系被称作关联分析或者关联规则学习。Apriori算法可以解决计算代价极高的物品组合问题，从而在合理的时间范围内找到频繁项集和关联规则。