基于Weka-JUNG框架高层数据结构的FP-Growth算法实现

Shui Wang, Le Wang
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引用次数: 12

摘要

FP-Growth是一种经典的数据挖掘算法;它目前的大多数实现都是基于编程语言的原始数据类型的数据结构;这会导致代码的可读性和可重用性很差。Weka是一个数据挖掘的开源平台,但缺乏处理树状结构数据的能力;JUNG是一个网络/图计算框架。从分析Weka的基础类入手,基于Weka- jung框架的高层面向对象数据对象,构建了FP-Growth算法的简洁实现;与Weka的内置Apriori实现进行了对比实验,验证了其正确性。这个实现已经作为一个开源的Google Code项目发布。
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An Implementation of FP-Growth Algorithm Based on High Level Data Structures of Weka-JUNG Framework
FP-Growth is a classical data mining algorithm; most of its current implementations are based on programming language's primitive data types for their data structures; this leads to poor readability & reusability of the codes. Weka is an open source platform for data mining, but lacks of the ability in dealing with tree-structured data; JUNG is a network/graph computation framework. Starting from the analysis on Weka's foundation classes, builds a concise implementation for FP-Growth algorithm based on high level object-oriented data objects of the Weka-JUNG framework; comparison experiments against Weka's built-in Apriori implementation are carried out and its correctness is verified. This implementation has been published as an open source Google Code project.
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