Customer Segmentation through Path Reconstruction

Abstract

This paper deals with the automatic classification of customers on the basis of their movements around a sports shop center. We start by collecting coordinates from customers while they visit the store. Consequently, any costumer’s path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. A guess about the trajectory is constructed, and a number of parameters are calculated before performing a Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. We can also monitor the state of the shop, identify different situations that appear during limited periods of time, and predict peaks in customer traffic.

K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region

“…Clustering allows for in-depth interpretation with many implications about which data should be targeted with specific data that is most likely to be of interest. [2]. Partition clustering algorithms, such as K means assign objects into k (a predetermined number of clusters) clusters, and reallocate objects iteratively to improve the quality of the clustering results.…”

“…The idea of a density-based clustering algorithm is that, for any point of a cluster, the neighborhood of a given distance unit must contain at least a minimum number of points. The application of cluster analysis is widely applied in various fields such as customer data clustering [2], [4]- [6], crop productivity mapping [7], agricultural data [3], palm oil production results [8], fruit yield grouping [9] port [10] and others. Data mining clustering techniques such as K-Means is one of the algorithms that are widely applied by many researchers including [11] using clustering algorithms for data grouping, data mapping, data classification, and so on.…”