搜索资源列表
zjyfpgrowth
- 我编写的能够实现频繁关联模式挖掘的FP-Growth数据挖掘算法。-prepared by the association to achieve frequent pattern mining FP-Growth data mining algorithms.
zlsj
- 滑动窗口的数据流闭合频繁模式的挖掘算法，用VC编写-sliding window of data flow closure frequent pattern mining algorithm, prepared by VC
FP-TREE
- FP-TREE算法，非常经典的数据挖掘算法，学习数据挖掘的好例子-FP-TREE algorithm, a very classic data mining algorithms, data mining study a good example of
ch4
- 时间序列模式挖掘，有GSP,PrefixSpan等算法。-Time series pattern mining, there are GSP, PrefixSpan algorithm.
PrefixSpan
- java版的PrefixSpan算法实现，文件里包含了详细的文档说明，还有示例。-PrefixSpan algorithm. The document containing a detailed descr iption and an example.
apriori_modify
- 数据挖掘中频繁模式挖掘的经典算法，根据别人代码添加了项长度的控制。-frequent pattern mining algorithm:Apriori. I extend a pattern-length-control function to it based on an existing code.
TPatternMiner
- Trajectory Pattern Mining-This software is an implementation of the T-Pattern mining algorithm. Reference paper is "Trajectory Pattern Mining", by F. Giannotti, M. Nanni, D. Pedreschi and F. Pinelli, published on KDD 2007 conference. This soft
SimpleAprioriCode
- its most famous apriori algorithm for sequence pattern mining.
knnsearch
- 寻找测试样本的最近邻，可以有效的用于用于模式识别，信号处理-This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing. The code was initially
PrefixSpan
- 列模式挖掘的PrefixSpan算法，用于对序列模式的挖掘-PrefixSpan out pattern mining algorithm
apriori
- Apriori algorithm for sequential pattern mining
Apriori--Algorithm
- 一种基于Apriori原理的算法的实现，它是序列模式挖掘中的经典算法-Apriori algorithm based on the realization of the principle, which is the classic sequential pattern mining algorithm
py_gapbide
- python实现的BIDE算法，应用于序列模式的挖掘-the algorithm is applied by python,it is applied to the sequential pattern mining
d4ef13.ZIP
- 界标窗口中数据流频繁模式挖掘算法研究A window in the data stream frequent pattern mining algorithm-A window in the data stream frequent pattern mining algorithm
data-mining-technology
- 数据挖掘是知识发现过程的一个基本步 骤。KDD是一门交叉学科，它涉及统计学、数据库技术、计算机科学、模式识别、人工智能、机器学习等多个学科。 -Data mining is a fundamental step in the knowledge discovery process. KDD is an interdisciplinary, it involves statistics, database technology, computer science, pattern reco
matlab-data-mining
- 数据挖掘(Data Mining)阶段首先要确定挖掘的任务或目的。数据挖掘的目的就是得出隐藏在数据中的有价值的信息。数据挖掘是一门涉及面很广的交叉学科，包括器学习、数理统计、神经网络、数据库、模式识别、粗糙集、模糊数学等相关技术。它也常被称为“知识发现”。知识发现(KDD)被认为是从数据中发现有用知识的整个过程。数据挖掘被认为是KDD过程中的一个特定步骤，它用专门算法从数据中抽取模式(patter，如数据分类、聚类、关联规则发现或序列模式发现等。数据挖掘主要步骤是：数据准备、数据挖掘、结果的解释
FpTree
- 频繁模式挖掘的demo，主要实现了频繁模式挖掘的树的构建算法。包含自定义的数据结构。实现了fp-growth算法。-Frequent pattern mining demo, frequent pattern mining tree algorithm. Contains the custom data structures. Fp-growth algorithm.
apriori
- 数据挖掘中频繁模式挖掘经典算法apriori-Data Mining frequent pattern mining algorithms apriori classic
Apriori
- 频繁模式挖掘Apriori算法 其中的.dat文件为样例数据, 具体使用方法参见main.c-Frequent Pattern Mining Apriori Algorithm
Frequent-Itemset-Mining
- 在数据挖掘频繁模式挖掘问题,典型算法的分析,是伪代码的分析,不是实际可以运行的代码，主要给的思路-Frequent pattern mining problem in data mining, the main algorithm analysis for typical algorithm, is the analysis of the pseudo code, give a train of thought