A TABU SEARCH APPROACH TO THE CLUSTERING PROBLEM

In this paper we consider the problem of clustering m objects into c clusters. The objects are represented by points in an n-dimensional Euclidean space, and the objective is to classify these m points into c clusters such that the distance between points within a cluster and its center (which is to be found) is minimized. The problem is a nonconvex program that has many local minima. It has been studied by many researchers and the most well-known algorithm for solving it is the k-means algorithm. In this paper, we develop a new algorithm for solving this problem based on a tabu search technique. Preliminary computational experience on the developed algorithm are encouraging and compare favorably with both the k-means and the simulated annealing algorithms

A Hard Clustering Approach To The Part Family Formation Problem

The part family problem in group technology can be stated as the problem of finding the best grouping of parts into families such that the parts within each family are as similar to each other as possible. In this paper, the part family formation problem is considered. The problem is cast into a hard clustering model, and the K-means algorithm is proposed for solving it. Preliminary computational experience on the algorithm is very encouraging and it shows that real-life problems of large sizes can efficiently be handled by this approach

A Hard Clustering Approach To The Part Family Formation Problem

The part family problem in group technology can be stated as the problem of finding the best grouping of parts into families such that the parts within each family are as similar to each other as possible. In this paper, the part family formation problem is considered. The problem is cast into a hard clustering model, and the K-means algorithm is proposed for solving it. Preliminary computational experience on the algorithm is very encouraging and it shows that real-life problems of large sizes can efficiently be handled by this approach

Computational Experience On Four Algorithms For The Hard Clustering Problem

In this paper, we consider the problem of clustering m objects in c clusters. The objects are represented by points in n-dimensional Euclidean space, and the objective is to classify these m points into c clusters such that the distance between points within a cluster and its center is minimized. The problem is a difficult optimization problem due to the fact that: it posseses many local minima. Several algorithms have been developed to solve this problem which include the k-means algorithm, the simulated annealing algorithm, the tabu search algorithm, and the genetic algorithm. In this paper, we study the four algorithms and compare their computational performance for the clustering problem. We test these algorithms on several clustering problems from the literature as well as several random problems and we report on our computational experience

A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM

In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions which are usually what the K-means algorithm obtains. The simulated annealing approach for solving optimization problems is described and is proposed for solving the clustering problem. The parameters of the algorithm are discussed in detail and it is shown that the algorithm converges to a global solution of the clustering problem. We also find optimal parameters values for a specific class of data sets and give recommendations on the choice of parameters for general data sets. Finally, advantages and disadvantages of the approach are presented

A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM

In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions which are usually what the K-means algorithm obtains. The simulated annealing approach for solving optimization problems is described and is proposed for solving the clustering problem. The parameters of the algorithm are discussed in detail and it is shown that the algorithm converges to a global solution of the clustering problem. We also find optimal parameters values for a specific class of data sets and give recommendations on the choice of parameters for general data sets. Finally, advantages and disadvantages of the approach are presented

A GLOBAL ALGORITHM FOR THE FUZZY CLUSTERING PROBLEM

The Fuzzy clustering (FC) problem is a non-convex mathematical program which usually possesses several local minima. The global minimum solution of the problem is found using a simulated annealing-based algorithm. Some preliminary computational experiments are reported and the solution is compared with that generated by the Fuzzy C-means algorithm

Computational Experience On Four Algorithms For The Hard Clustering Problem

In this paper, we consider the problem of clustering m objects in c clusters. The objects are represented by points in n-dimensional Euclidean space, and the objective is to classify these m points into c clusters such that the distance between points within a cluster and its center is minimized. The problem is a difficult optimization problem due to the fact that: it posseses many local minima. Several algorithms have been developed to solve this problem which include the k-means algorithm, the simulated annealing algorithm, the tabu search algorithm, and the genetic algorithm. In this paper, we study the four algorithms and compare their computational performance for the clustering problem. We test these algorithms on several clustering problems from the literature as well as several random problems and we report on our computational experience