Description: 该算法可以用vc实现Apriori算法的全部．请大家放心使用！-the algorithm can be used vc Apriori algorithm to achieve the full. Please rest assured use! Platform: |
Size: 67538 |
Author:小强强 |
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Description: 本算法的基本功能是用C＋＋语言实现了APRIORI算法，用户可以先选择要进行的操作。然后再输入支持度和置信度，就可得到挖掘的结果。
输出的结果主要包括两个部分。
1．输出所有的频繁项集。
2．输出所有的产生的规则。
算法还能够输出初始的事务集合，并且可以输出产生的中间结果。-the algorithm is the basic functions using C + + language of APRIORI algorithm, Users may choose to conduct the operation. Then import support and confidence, could be the result of excavation. Output of the two main parts. 1. Output of all frequent sets. 2. Output of all the rules. Algorithm can also output the affairs of the initial pool, and can output of intermediate results. Platform: |
Size: 59589 |
Author:linvg |
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Description: 用vc实现Apriori算法的全部功能．请大家尽情心使用！-the algorithm can be used vc Apriori algorithm to achieve the full. Please rest assured use! Platform: |
Size: 65953 |
Author:yangfeihua |
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Description: 这是一个数据挖掘中的关联规则挖掘的经典算法：Apriori算法的程序-This is the code of Apriori-the classical algorithm used in Association Rules of Data Mining Platform: |
Size: 694272 |
Author:金水湾 |
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Description: 这是关于数据仓库与数据挖掘的Apriori算法的实现程序，基于关系型数据库的。-This is the implementation of Apriori Algorithm used in Data Warehouse and Data Mining filed, based on the Relation Database. Platform: |
Size: 428032 |
Author:孙江萍 |
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Description: 数据挖掘算法 关联规则算法 apriori算法（c语言版本）-algorithm of data mining
algorithm of corelationship rule
algorithm of aproori
(C language edition) Platform: |
Size: 123904 |
Author:万喜 |
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Description: 用C实现的APRIORI算法，希望对学习数据结构和算法的朋友有所帮助-C analysis algorithm to achieve the hopes of learning algorithms and data structures help a friend Platform: |
Size: 122880 |
Author:yu |
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Description: 收集数据：使用任何方法
准备数据：任意数据类型都可以，因为我们只保存集合
分析数据：使用任何方法
训练算法：使用Apriori算法来找到频繁项集
测试算法：不需要测试过程
使用算法：用于发现频繁项集以及物品之间的关联规则
使用Apriori算法，首先计算出单个元素的支持度，然后选出单个元素置信度大于我们要求的数值，比如0.5或是0.7等。然后增加单个元素组合的个数，只要组合项的支持度大于我们要求的数值就把它加到我们的频繁项集中，依次递归。
然后根据计算的支持度选出来的频繁项集来生成关联规则。(# Python 3 Implementation of Apriori algorithm
This program is based on [Aaron Zira's implementation of Apriori algorithm](https://github.com/aaronzira/apriori) and is adapted for use in other python 3 programs
## Dependencies
This program uses [_demjson.py_](https://github.com/dmeranda/demjson/blob/master/demjson.py) to write matrix into file
* Install with
```bash
pip3 install demjson
```
## Usage
* Initialize and learn frequency using data from file
```python 3
# data: path of data source file
# out: path of output file
AP = apriori.APriori(data='./test_datasets/transactions.dat',
out='./test_datasets/result.txt')
# This function will write Data into output file
AP.find_frequent(support=50, min_set_size=2, max_set_size=3)) Platform: |
Size: 1324032 |
Author:wingnut |
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Description: 经典的Apriori算法由于要多次扫描数据库，产生大量的候选项集，极大的影响了算法的时间和空间效率。为了减少数据库的扫描次数，可采用矩阵记录所需数据并放入内存中。矩阵record的每个行号对应一个商品码（即一个项），每个列号对应一个客户号（即一个事务）。倘若第j个客户买了第i个商品，则record[i][j]=1,否则record[i][j]=0. 扫描一遍数据库，将矩阵record初始化。接下来的工作，都可以通过扫描内存中的矩阵进行，不必再扫描数据库。(The classic Apriori algorithm generates many candidate itemsets because it needs to scan the database many times, which greatly affects the time and space efficiency of the algorithm. In order to reduce the number of database scanning, we can use matrix to record the required data and put it into memory. Every row number of matrix record corresponds to a commodity code, that is, one item, and each column number corresponds to a customer number (i.e. a transaction). If a j customer buys a I commodity, then record[i][j]=1, otherwise record[i][j]=0. scan the database once and initialize the matrix record. The next work can be done by scanning the matrix in memory without scanning the database again.) Platform: |
Size: 23552 |
Author:等朕称了帝 |
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Description: 经典的apriori算法，用于挖掘数据中最大频繁项集和生成关联规则(The classic Apriori algorithm is used to mine the largest frequent itemsets and generate association rules in data.) Platform: |
Size: 125952 |
Author:我觉得可以 |
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Description: apriori算法python代码实现，需用数据集进行测试(Apriori algorithm Python code implementation, you need to take the data set to test.) Platform: |
Size: 2048 |
Author:zbyyyyyy |
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