Constrained clustering by constraint programming

Abstract

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 integration of different kinds of constraints, they are usually based on declarative frameworks and they are often exact methods, which either enumerate all the solutions satisfying the user constraints, or find a global optimum when an optimization criterion is specified. In a previous work, we have proposed a model for Constrained Clustering based on a Constraint Programming framework. It is declarative, allowing a user to integrate user constraints and to choose an optimization criterion among several ones. In this article we present a new and substantially improved model for Constrained Clustering, still based on a Constraint Programming framework. It differs from our earlier model in the way partitions are represented by means of variables and constraints. It is also more flexible since the number of clusters does not need to be set beforehand; only a lower and an upper bound on the number of clusters have to be provided. In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms. We show that such a framework can easily be embedded in a more general process and we illustrate this on the problem of finding the optimal Pareto front of a bi-criterion constrained clustering task. We compare our approach with existing exact approaches, based either on a branch-and-bound approach or on graph coloring on twelve datasets. Experiments show that the model outperforms exact approaches in most cases.

Constrained distance based clustering for time-series: a comparative and experimental study

“…This framework enables the modeling of different constrained clustering problems, by specifying an optimisation criterion and by setting the user constraints. The framework is evolved by improving the model and by developing dedicated propagation algorithms for each optimisation criterion (Dao et al, 2017). In this model, the number of clusters K does not need to be fixed beforehand, only bounds are needed K min ≤ K ≤ K max and the model has three components: partition constraints, user constraints, and objective function constraints.…”

“…This problem aims to find clusters that are both compact (minimising the maximal diameter) and well separated (maximising the split), under user constraints. In (Dao et al, 2017) it is shown that to solve this problem, the framework can be used by iteratively changing the objective function and adding constraints on the other objective value. This framework has been extended to integrate user constraints on properties, in order to make clustering actionable .…”

Constrained Image Generation Using Binarized Neural Networks with Decision Procedures

“…The first one uses constraint to enhance machine learning techniques with declarative constraints, e.g. in solving constrained clustering problems and in data mining techniques that handle domain specific constraints [19,20,21]. One recent example is the work of Ganji et al [20] who proposed a logical model for constrained community detection.…”

Equitable Conceptual Clustering Using OWA Operator

“…Later, Chabert et al have introduced two new CP models for computing optimal conceptual clusterings. The first model (denoted FullCP2) may be seen as an improvement of [6]. The second model (denoted HybridCP) follows the two step approach of [17] : the first step is exactly the same; the second step uses CP to select formal concepts.…”

“…Distance-based clustering aims at finding homogeneous clusters only based on a dissimilarity measure between objects. Different declarative frameworks have been developed, which rely on CP [6] or ILP [1,15]. There are a few existing approaches for obtaining balanced clusters.…”