Data-driven decision-making has garnered growing interest as a result of the increasing
availability of data in recent years. With that growth many opportunities and challenges have …

Constrained Clustering allows to make the clustering task more accurate by integrating user
constraints, which can be instance-level or cluster-level constraints. Few works consider the …

Sales forecasting is central to the operations of most companies. Especially important is the
forecasting of new product sales, because these forecasts guide many decisions that …

In this paper a new method of preprocessing incomplete data is introduced. The method is
based on clusterwise linear regression and it combines two well-known approaches for …

In clusterwise linear regression (CLR), the aim is to simultaneously partition data into a
given number of clusters and to find regression coefficients for each cluster. In this paper, we …

Clusterwise linear regression (CLR), a clustering problem intertwined with regression, finds
clusters of entities such that the overall sum of squared errors from regressions performed …

The clusterwise linear regression problem is formulated as a nonsmooth nonconvex
optimization problem using the squared regression error function. The objective function in …

We propose an algorithm based on an incremental approach and smoothing techniques to
solve clusterwise linear regression (CLR) problems. This algorithm incrementally divides the …

Clusterwise linear regression (CLR) is a well-known technique for approximating a data
using more than one linear function. It is based on the combination of clustering and multiple …

A column generation based approach is proposed for solving the cluster-wise regression
problem. The proposed strategy relies firstly on several efficient heuristic strategies to insert …