Sentence examples for customers clustering from inspiring English sources

Since we do not know the number of target items to be tracked (new items may be added to the shopping cart or some previous items are discard by customers at any time, and we also do not know the number of customers), clustering algorithms, such as k-means [12], in which the number of clusters must be known as a priori, thus leading to this kind of clustering algorithm that can not be applied.

In warm weather, customers cluster around small wooden tables on the street.

The screen door slams regularly, and customers cluster around the glass-fronted display case.

Customers cluster there, as a manager pressures them to make a choice.

Customer clustering is an essential step to reduce the complexity of large-scale logistics network optimization.

However, due to the heterogeneity and high-dimension of customers' characteristics, the customer clustering problem has not been widely studied.

This paper presents a fuzzy-based customer clustering algorithm with a hierarchical analysis structure to address this issue.

We propose a collaborative e-Work based optimization approach for assisting the strategic logistic network design problem, which considers fleet design and customer clustering decisions.

Normally, fleet design and customer clustering decisions are made by mid-level logistics managers while network design decision is made independently by high-level logistics managers.

Results from a case study in Anshun, China reveal that the proposed approach outperforms the other three prevailing algorithms to resolve the customer clustering problem.

On the other hand, tactical fleet design and customer clustering decisions are modeled based on a Hub and Spoke cost structure.