TY - GEN
T1 - Discovering probabilistic weighted frequent itemsets over uncertain data
AU - You, Tao
AU - Li, Tingfeng
AU - Du, Chenglie
AU - Zhai, Xiang
AU - Jiang, Nan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/21
Y1 - 2018/6/21
N2 - The uncertain data management and mining is a growing research topic in recent years. To mine more meaningful patterns, some algorithms have considered the importance of every items as a constraint. None of them have been, however, designed to discover patterns in reasonable such as Possible World Semantics (PWS) which has usually adopted. In this paper, we defined the weighted probabilistic of frequent itemsets, which provides a better view on how to obtain the more interesting patterns under PWS. In terms of the concept, a deepth-first algorithm PWFIM is proposed to generate the results, and we also designed a Dynamic Programming method and several pruning methods to further improve the mining performance. We have carried out substantive experiments on real life and synthetic data sets. The results show that the proposed algorithm can be more meaningful and interesting than other data algorithms. We also evaluated the performance of the algorithm at runtime, consumption of memory, and number of patterns.
AB - The uncertain data management and mining is a growing research topic in recent years. To mine more meaningful patterns, some algorithms have considered the importance of every items as a constraint. None of them have been, however, designed to discover patterns in reasonable such as Possible World Semantics (PWS) which has usually adopted. In this paper, we defined the weighted probabilistic of frequent itemsets, which provides a better view on how to obtain the more interesting patterns under PWS. In terms of the concept, a deepth-first algorithm PWFIM is proposed to generate the results, and we also designed a Dynamic Programming method and several pruning methods to further improve the mining performance. We have carried out substantive experiments on real life and synthetic data sets. The results show that the proposed algorithm can be more meaningful and interesting than other data algorithms. We also evaluated the performance of the algorithm at runtime, consumption of memory, and number of patterns.
KW - Data mining
KW - Possible World Semantics
KW - Uncertain database
KW - Weighted probabilistic frequent itemset
UR - http://www.scopus.com/inward/record.url?scp=85050188178&partnerID=8YFLogxK
U2 - 10.1109/FSKD.2017.8393027
DO - 10.1109/FSKD.2017.8393027
M3 - 会议稿件
AN - SCOPUS:85050188178
T3 - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
SP - 1728
EP - 1734
BT - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
A2 - Zhao, Liang
A2 - Wang, Lipo
A2 - Cai, Guoyong
A2 - Li, Kenli
A2 - Liu, Yong
A2 - Xiao, Guoqing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Y2 - 29 July 2017 through 31 July 2017
ER -