Title
Fast $q$-gram Mining on SLP Compressed Strings
Abstract
We present simple and efficient algorithms for calculating $q$-gram frequencies on strings represented in compressed form, namely, as a straight line program (SLP). Given an SLP of size $n$ that represents string $T$, we present an $O(qn)$ time and space algorithm that computes the occurrence frequencies of $q$-grams in $T$. Computational experiments show that our algorithm and its variation are practical for small $q$, actually running faster on various real string data, compared to algorithms that work on the uncompressed text. We also discuss applications in data mining and classification of string data, for which our algorithms can be useful.
Year
Venue
Keywords
2011
Clinical Orthopaedics and Related Research
computer experiment,data mining,data structure
DocType
Volume
Citations 
Journal
abs/1103.3
18
PageRank 
References 
Authors
0.98
17
4
Name
Order
Citations
PageRank
Keisuke Goto19813.93
Hideo Bannai262079.87
Shunsuke Inenaga359579.02
Masayuki Takeda490279.24