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In this paper we evaluate metaheuristic optimization methods on a partitional clustering task of a real-world supply chain dataset, aiming at customer segmentation. For this purpose, we rely on the automatic clustering framework proposed by Das et al. [1], named henceforth DAK framework, by testing its performance for seven different metaheuristic optimization algorithm, namely: simulated annealing...
This paper presents a new heuristic for the data clustering problem. It comprises two parts. The first part is a greedy algorithm, which selects the data points that can act as the centroids of well-separated clusters. The second part is a single-solution-based heuristic, which performs clustering with the objective of optimizing a cluster validity index. Single-solution-based heuristics are memory...
An island partitioning method based on cloud adaptive genetic algorithm is proposed. The traditional genetic algorithm is modified by making use of the trait of cloud theory, which shows both randomness and a tendency of stability. The traditional crossover operator is replaced by cloud crossover operator, aiming at improving global searching ability and avoiding falling into local minimums. The concept...
Problem of community detection has attracted many research efforts in recent years. Most of the algorithms developed for this purpose, take advantage of single-objective optimization methods which may be ineffective for complex networks. In addition, most of the networks in the real world are weighted, and therefore, this fact must be of special interest in order to achieve more precise communities...
This paper presents a survey of Hybrid fuzzy c-means (FCM) clustering algorithms, The algorithmic steps, parameters involved in the algorithm & the experimental results on various datasets of several hybrid clustering methods are discussed in this paper. Hybrid FCM clustering techniques are obtained by modifying the FCM either by incorporating hesitation degree of Intuionistic approach or by replacing...
K-Means has been paid attention to many areas recently, however, it is easy to fall into local optimum and the outliers influence the final results. This paper proposes an improved method for k-means clustering. Different from the traditional k-means algorithms, in our algorithm both intracluster compactness and intercluster separation are considered in our new presented method. A new model is established...
The regionalization problem involves aggregating several spatially contiguous basic geographical units into regions while optimizing a defined objective. Equity is one of its most important constraints with the aim to ensure the value of one or several interesting spatial attributes at a certain level and to provide solutions to specific requirements in applications. This paper tackles a new regionalization...
Clustering is an unsupervised technique, which partitions the entire input space into regions. These initial partitions have a great impact on the resulting clusters. In this paper, a new Multi Stage Genetic Clustering (MSGC) scheme for multiobjective optimization in data clustering is proposed, which can automatically partition the data into an appropriate number of clusters. K-means is a well-known...
One of the great aspirations of machine learning is the clustering methods. It consists on categorized a set of similar data into different groups based on related properties. The clustering ensemble is used in aim to improve the performance and the stability of the unsupervised classification methods through the concept of weighting. One of the major problems in clustering ensembles is the consensus...
Menu construction is an important task for institutions that need to plan menus within certain constraints. There is also a personal need for professional menu construction by clients or patients who should eat according to a planned diet. For menu construction and dietary analysis, there are several approaches (e.g., linear programming, genetic algorithms, rule-based expert systems, etc.) and commercial...
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