The Benefits of Using Prefix Tree Data Structure in Multi-Level Frequent Pattern Mining

2007 ◽  
Author(s):  
Mirela Pater ◽  
Daniela E. Popescu
Keyword(s):  
Data Structure ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Prefix Tree ◽  
Tree Data ◽  
Multi Level ◽  
Tree Data Structure

A Scalable Algorithm for Constructing Frequent Pattern Tree

2014 ◽  
Vol 10 (1) ◽  
pp. 42-56 ◽  
Author(s):  
Zailani Abdullah ◽  
Tutut Herawan ◽  
A. Noraziah ◽  
Mustafa Mat Deris
Keyword(s):  
Data Structure ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Scalable Algorithm ◽  
Tree Construction ◽  
Frequent Pattern Tree ◽  
Support Threshold ◽  
Benchmark Datasets ◽  
Tree Data ◽  
Tree Data Structure

Frequent Pattern Tree (FP-Tree) is a compact data structure of representing frequent itemsets. The construction of FP-Tree is very important prior to frequent patterns mining. However, there have been too limited efforts specifically focused on constructing FP-Tree data structure beyond from its original database. In typical FP-Tree construction, besides the prior knowledge on support threshold, it also requires two database scans; first to build and sort the frequent patterns and second to build its prefix paths. Thus, twice database scanning is a key and major limitation in completing the construction of FP-Tree. Therefore, this paper suggests scalable Trie Transformation Technique Algorithm (T3A) to convert our predefined tree data structure, Disorder Support Trie Itemset (DOSTrieIT) into FP-Tree. Experiment results through two UCI benchmark datasets show that the proposed T3A generates FP-Tree up to 3 magnitudes faster than that the benchmarked FP-Growth.

Maintenance of Frequent Patterns

2009 ◽  
pp. 273-293 ◽  
Author(s):  
Mengling Feng ◽  
Jinyan Li ◽  
Guozhu Dong ◽  
Limsoon Wong
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Research Opportunities ◽  
Prefix Tree ◽  
Emerging Trends ◽  
Maintenance Problem

This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern maintenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori-based, partition-based, prefix-tree-based, and conciserepresentation- based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pattern maintenance are also discussed.

scholarly journals Novel Approach for Frequent Pattern Algorithm for Maximizing Frequent Patterns in Effective Time

2012 ◽  
Vol 3 (2) ◽  
pp. 279-283
Author(s):  
Rahul Sharma ◽  
Dr. Manish Manoria
Keyword(s):  
Data Structure ◽  
Large Datasets ◽  
Experimental Results ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Effective Time ◽  
Novel Approach ◽  
Tree Data ◽  
Improved Performance ◽  
Tree Data Structure

The essential aspect of mining association rules is to mine the frequent patterns. Due to native difficulty it is impossible to mine complete frequent patterns from a dense database. FP-growth algorithm has been implemented using an Array-based structure, known as the FP-tree,which is for storing compressed frequency information. Numerous experimental results have demonstrated that the algorithm performs extremely well. But in FP-growth algorithm, two traversals of FP-tree are needed for constructing the new conditional FP-tree. In this paper we present a novel Array Based Without Scanning Frequent Pattern (ABWSFP) tree technique that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FP-tree based algorithms. The technique works especially well for large datasets. We then present a new algorithm which use the QFP-tree data structure in combination with the FP Tree- Experimental results show that the new algorithm outperform other algorithm in not only the speed of algorithms, but also their CPU consumption and their scalability.

Efficient frequent pattern mining based on Linear Prefix tree

2014 ◽  
Vol 55 ◽  
pp. 125-139 ◽  
Author(s):  
Gwangbum Pyun ◽  
Unil Yun ◽  
Keun Ho Ryu
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Prefix Tree
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