An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database

Demetrovics, Janos, Thi, Vu Duc, and Duong, Tran Huy. "An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database." Serdica Journal of Computing 9.2 (2015): 105-122. <http://eudml.org/doc/281501>.

@article{Demetrovics2015,
abstract = {Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the number of occurrences of the sequences (supports of sequences), but also concerned about importance of sequences (weights). When generating subsequence candidates we use both the support and the weight of the candidates while maintaining the downward closure property of these patterns which allows to accelerate the process of candidate generation.},
author = {Demetrovics, Janos, Thi, Vu Duc, Duong, Tran Huy},
journal = {Serdica Journal of Computing},
keywords = {Data Mining; Frequent Sequential Patterns; Weighted; Sequential Patterns},
language = {eng},
number = {2},
pages = {105-122},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database},
url = {http://eudml.org/doc/281501},
volume = {9},
year = {2015},
}

TY - JOUR
AU - Demetrovics, Janos
AU - Thi, Vu Duc
AU - Duong, Tran Huy
TI - An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database
JO - Serdica Journal of Computing
PY - 2015
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 9
IS - 2
SP - 105
EP - 122
AB - Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the number of occurrences of the sequences (supports of sequences), but also concerned about importance of sequences (weights). When generating subsequence candidates we use both the support and the weight of the candidates while maintaining the downward closure property of these patterns which allows to accelerate the process of candidate generation.
LA - eng
KW - Data Mining; Frequent Sequential Patterns; Weighted; Sequential Patterns
UR - http://eudml.org/doc/281501
ER -