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  1. Proceedings of the 1st international workshop on open source data mining (OSDM '05)
  2. An implementation of the FP-growth algorithm
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Finding the real patterns
An implementation of the FP-growth algorithm
MoSS: a program for molecular substructure mining
Implementing leap traversals of the itemset lattice
PLWAP sequential mining: open source code
On benchmarking frequent itemset mining algorithms: from measurement to analysis
On the effectiveness and efficiency of computing bounds on the support of item-sets in the frequent item-sets mining problem
A trie-based APRIORI implementation for mining frequent item sequences
Keeping things simple: finding frequent item sets by recursive elimination
Subdue: compression-based frequent pattern discovery in graph data
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining

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An implementation of the FP-growth algorithm

Content Provider ACM Digital Library
Author Borgelt, Christian
Abstract The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. In this paper I describe a C implementation of this algorithm, which contains two variants of the core operation of computing a projection of an FP-tree (the fundamental data structure of the FP-growth algorithm). In addition, projected FP-trees are (optionally) pruned by removing items that have become infrequent due to the projection (an approach that has been called FP-Bonsai). I report experimental results comparing this implementation of the FP-growth algorithm with three other frequent item set mining algorithms I implemented (Apriori, Eclat, and Relim).
Starting Page 1
Ending Page 5
Page Count 5
File Format PDF
ISBN 1595932100
DOI 10.1145/1133905.1133907
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2005-08-21
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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