Wavelet trees: A survey

Christos Makris

doi:10.2298/csis110606004m

Wavelet trees: A survey

Computer Science and Information Systems ◽

10.2298/csis110606004m ◽

2012 ◽

Vol 9 (2) ◽

pp. 585-625 ◽

Cited By ~ 35

Author(s):

Christos Makris

Keyword(s):

Data Structure ◽

Specific Area ◽

Research Area ◽

Text Compression ◽

Text Indexing ◽

Indexing And Retrieval ◽

Wide Range ◽

Tree Data ◽

Tree Data Structure ◽

Young Researchers

The topic of this paper is the exploration of the various characteristics of the wavelet tree data structure, a data structure that was initially proposed for text compression applications but has found a plethora of other uses in text indexing and retrieval. Issues concerning the efficient maintenance of the structure, plus its handling in various applications are explored. Our main aim is to provide to computer science researchers that would like to explore the specific area, an up-to-date comprehensive material covering a wide range of applications. This kind of up-to-date survey is missing from the current bibliography and we hope that it will help young researchers to get familiar with the notions of this research area.

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A Scalable Algorithm for Constructing Frequent Pattern Tree

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2014010103 ◽

2014 ◽

Vol 10 (1) ◽

pp. 42-56 ◽

Cited By ~ 3

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.

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