Description: A program to find frequent molecular substructures and discriminative fragments in a database of molecule descriptions. The algorithm is based on the Eclat algorithm for frequent item set mining.-A program to find frequent molecular (subst ructures and discriminative fragments in a dat abase of molecule descriptions. The algorithm is based on the algorithm for frequent Eclat ite m set mining. Platform: |
Size: 343040 |
Author:无心 |
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Description: Apriori算法是发现关联规则领域的经典算法。该算法将发现关联规则的过程分为两个步骤：第一步通过迭代，检索出事务数据库中的所有频繁项集，即支持度不低于用户设定的阈值的项集；第二步利用频繁项集构造出满足用户最小信任度的规则-Apriori association rules algorithm is found in the field of classical algorithms. The algorithm will find the process of association rules is divided into two steps: first, through iteration, retrieve a transaction database of all the frequent itemsets, that is, support for no less than user-set threshold itemset the second step use of frequent itemsets constructed to meet the users trust in the rules of the smallest Platform: |
Size: 11264 |
Author:hey |
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Description: Adwin is Adaptive sliding window model designed in java for frequent item set mining over data streams Platform: |
Size: 5120 |
Author:zia |
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Description: FP增长算法的实现与测试(Java实现)
1、程序编译运行环境Eclipse3.20+JDK1.60
2、程序参数说明
-F=filename
-S=support
-C=confidence
filename:数据集文件名，必须位于工程根目录下
support:支持度，位于0-100.0之间的任意数
confidence:置信度，位于0-100.0之间的任意数
例如：-F=anonymous-msweb.data -S=10.0 -C=45.0（参数顺序无关）
3. 程序正确性验证
工程中包含sample.txt文件用来验证。
具体方法：
（1）在AssociationRuleMining 类中，preprocessDataSet函数的最后一条语句替换为fileName = "sample.txt"
（2）在FPgrowth类中，main函数中的 myFPtree.outputARs2() 替换为 myFPtree.outputARs()
（3）输入正确格式的参数，数据集文件名可任意-FP growth algorithm implementation and testing (Java implementation)
1, compiled runtime environment Eclipse3.20+ JDK1.60
2, program parameters that
-F = filename
-S = support
-C = confidence
filename: data set file name, must be located project root directory
support: support, in any number between 0-100.0
confidence: confidence, any number in between 0-100.0
example:-F = anonymous-msweb.data-S = 10.0-C = 45.0 (parameter order has nothing to do)
3. program correctness verification project file contains sample.txt to verify. Specific methods:
(1) AssociationRuleMining class, preprocessDataSet last statement function is replaced fileName = " sample.txt"
(2) in the FPgrowth class, main function in the myFPtree.outputARs2 () replace myFPtree.outputARs ()
(3) Enter the correct format, parameters, file names can be arbitrary data set Platform: |
Size: 540672 |
Author:frank |
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Description: 数据挖掘中的关联算法FPtree在Java中的实现，能挖掘出数据集中的频繁模式-Data Mining association algorithm FP tree in Java to achieve, and can dig out the data set frequent pattern Platform: |
Size: 76800 |
Author:肖勇 |
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Search - java frequent set