A Hybrid Weighted Periodical Pattern Mining and Prediction for Personal Mobile Commerce
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
Academic Journals
Abstract
In case of large amount of the search engine based
applications, mobile e-commerce has established a more
interest under both industry and academia. From that mining
the user behavior and prediction of the user to analysis the
mobile commerce behaviors based on their actions are most
important. To perform these steps, previous work proposed a
novel structure called Mobile Commerce Explorer (MCE). It
can be performed in three ways 1) Similarity Inference
Model (SIM) for measuring the similarities amongst stores
and items 2) Personal Mobile Commerce Pattern Mine
(PMCP-Mine) algorithm for well-organized discovery of
mobile users Personal Mobile Commerce Patterns (PMCPs);
and 3) Mobile Commerce Behavior Predictor (MCBP) for
prediction of possible mobile user behaviors. Assigning the
weight values for each item in the mobile transaction
database finds the best frequent pattern mining from the
mobile user pattern .Proposed system considering the
different weight values for each item and the select the best
weight values to frequent pattern mining .Selection of best
weight values from the different weight values we use
particle swarm optimization algorithm system is initialized
with a population of random solution such as different
weight values and search for best weight values by updating
invention. The particle swarm optimization changing the
velocity of each particle toward finds best weight values at
both local and global locations. After finding the weight
values than derive the frequent pattern mining results from
the existing transaction behavior of each mobile users
.Weighted frequent pattern mining with PSO achieve an
wide-ranging investigational estimation by replication and
show that better accuracy outcome.
Description
Keywords
PMCP, WMCBP, PSO, Data mining, Mobile commerce
Citation
Preetha, D., Mythili, K., Karthika, D., RangaRaj, R., Priya, P.H., RangaRaj, R., Amuthajanaki, B., Jayalakshmi, K., Kahebo, M., Mujuni, E. and Mushi, A., 2013. A HYBRID WEIGHTED PERIODICAL PATTERN MINING AND PREDICTION FOR PERSONAL MOBILE COMMERCE. International Journal, 2(8).