Assume that a franchise plans to open k branches in a city, so that the average distance from each residential block to the closest branch is minimized. This is an instance of the k-medoids problem, where residential blocks constitute the input dataset and the k branch locations correspond to the medoids. Since the problem is NP-hard, research has focused on approximate solutions. Despite an av...

We consider a challenging clustering task: the clustering of multi-word terms without document co-occurrence information in order to form coherent groups of topics. For this task, we developed a methodology taking as input multi-word terms and lexico-syntactic relations between them. Our clustering algorithm, named CPCL is implemented in the TermWatch system. We compared CPCL to other existing ...

Medoids-based fuzzy relational clustering generates clusters of objects based on relational data, which records pairwise similarity or dissimilarities among objects. Compared with single-medoid based approaches, multiple-weighted medoids has shown superior performance in clustering. In this paper, we present a new version of fuzzy relational clustering in this family called fuzzy clustering wit...

Medoid clustering frequently gives better results than those of the K-means clustering in the sense that a unique object is the representative element of a cluster. Moreover the method of medoids can be applied to nonmetric cases such as weighted graphs that arise in analyzing SNS(Social Networking Service) networks. A general problem in clustering is that asymmetric measures of similarity or d...

This research proposed a new algorithm for clustering datasets using the Flexible K-Medoids Partitioning Method. The procedure is divided into two phases, selecting initial medoids and determining partitioned dataset. are selected based on block representation of combination sum deviation variable values. relative positions objects will be separated when values p variables different even though...

This paper presents Centre-based hard clustering approaches for clustering Y-STR data. Two classical partitioning techniques: Centroid-based partitioning technique and Representative object-based partitioning technique are evaluated. The k-Means and the k-Modes algorithms are the fundamental algorithms for the centroid-based partitioning technique, whereas the k-Medoids is a representative obje...

Appendix A. Notation N = {1, 2, ..., N} =: [N ] is the whole set of data points. i, j ∈ N denote points. dij := D(xi,xj). D is the number of data sets. Td ⊆ N denotes the set of points in the d-th dataset, i.e. ∪d=1Td = N . Nd = |Td| is the number of points in Dataset d. d(i) ∈ [D] denotes the dataset index of Point i. M ⊆ N is the set of medoids. k, l ∈ M denote clusters and themselves are med...

Empresas de tecnologia financeira, mais conhecidas como fintechs, são companhias inovação tecnológica com potencial transformador para o setor financial. Nelas, tratamento personalizado requer a análise quantidades expressivas dados. Dessa forma, utilizar técnicas mineração dados pode oferecer maior facilidade em classificar e visualizar os consumidores. A empresa analisada nesse artigo, Justa,...

The Power Trading Agent Competition (Power TAC) is a feature-rich simulation that simulates an energy market in a smart grid, where software brokers can buy energy in wholesale markets and sell energy in tariff markets to consumers. Successful brokers can maximize their profits by buying energy at low prices in the wholesale market and selling them at high prices to the consumers. However, this...

Cluster analysis may be performed when one wishes to group similar objects into a given number of clusters. Several algorithms are available in order to construct these clusters. In this talk, focus will be on the generalized k-means algorithm which has the classical k-means procedure as well as the k-medoids algorithm as particular cases. Among the outputs of these clustering techniques, a cla...