Using the k-d Tree Data Structure to Accelerate Monte Carlo Simulations

Qile P. Chen; Bai Xue; J. Ilja Siepmann

doi:10.1021/acs.jctc.6b01222

Using the k-d Tree Data Structure to Accelerate Monte Carlo Simulations

Journal of Chemical Theory and Computation ◽

10.1021/acs.jctc.6b01222 ◽

2017 ◽

Vol 13 (4) ◽

pp. 1556-1565 ◽

Cited By ~ 11

Author(s):

Qile P. Chen ◽

Bai Xue ◽

J. Ilja Siepmann

Keyword(s):

Monte Carlo ◽

Data Structure ◽

Monte Carlo Simulations ◽

Tree Data ◽

Tree Data Structure

Download Full-text

Self-avoiding random surfaces: Monte Carlo study using oct-tree data-structure

Journal of Physics A General Physics ◽

10.1088/0305-4470/24/19/024 ◽

1991 ◽

Vol 24 (19) ◽

pp. 4619-4635 ◽

Cited By ~ 25

Author(s):

J O'Connell ◽

F Sullivan ◽

D Libes ◽

E Orlandini ◽

M C Tesi ◽

...

Keyword(s):

Monte Carlo ◽

Data Structure ◽

Monte Carlo Study ◽

Random Surfaces ◽

Tree Data ◽

Tree Data Structure

Download Full-text

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.

Download Full-text