| dc.description.abstract | In the development of technology at this time, data processing has been massively
carried out by carrying out several approaches and data constructions. Data min-
ing is the process of extracting data from a large database so that it can be used to
build multivariate data, where more and more data produces increasingly complex
value variations. Identification of customers in the business sector really needs to
be done as an evaluation of a business that has been run in order to continue to
grow and be able to keep abreast of business developments in the same sector. In
this study, clustering of customers using rail mass transportation was carried out
using the deep constraint clustering approach. Deep constrained clustering com-
bines equality constraints between several customers to find out which of several
customers in the data set are in the same group (positive or must-link constraints)
and which are not in the same group (negative or cannot-link constraints). The
result achieved is the formation of 6 clusters in the population for multivariables,
which are obtained in the form of passenger clusters that have a tendency to use
the rail mass transportation mode. By knowing the characteristics of passengers
in each cluster, it can be considered for the services to be provided so as to be able
to provide increased business services that have been carried out. | en_US |
