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Clustering, a well-known technique, is used to divide a data set into number of groups, called clusters. Differential evolution and particle swarm optimization are robust, fast and very effective search techniques. To increase computational capability, two different quantum inspired meta-heuristics for automatic clustering, have been proposed here. An application of quantum inspired techniques has...
This study proposes a novel design and implementation of Differential Evolution (DE) using the Partitioned Global Address Space (PGAS) parallel computing model and the Unified Parallel C (UPC) programming language. The mapping of DE concepts to UPC features is presented and a DE useful for both many-core shared memory systems and clusters of computers with distributed memory is implemented and evaluated...
One of the most obvious features of social networks is their community structure. Several types of methods were developed for discovering communities in the networks, either from the global perspective or based on local information only. Local methods are appropriate when working with large and dynamic networks or when real-time results are expected. In this paper we explore two such methods and compare...
Searching of similar pictures was in the past based mainly on searching of similar picture names. We try to find an effective method how to search pictures by searching of similar information in the picture (histograms, shapes, blocks,). There already are some methods but still not effective enough. In this paper we describe a method where we combine vector quantization (VQ) and fuzzy S-trees. Work...
This study introduces a new soft computing method for expert identification in social networks based on formal concept analysis and fuzzy rules. Expert identification is an important task in social network analysis and there are several methods to identify people who have experience in given area. In this paper, we propose a hybrid approach where the formal concept analysis is used for finding author's...
Unsupervised clustering of large data sets is a complicated NP-hard task. Due to its complexity, various metaheuristic machine learning algorithms have been used to automate or aid the clustering process. Genetic and evolutionary algorithms have been deployed to find clusters in data sets with success. However, also evolutionary clustering suffers from the high computational demands when it comes...
Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures in real world networks and databases. In this paper, we use the spectral partitioning to detect communities in a co-authorship network. The partitioning depends heavily on the weighting of the underlying network...
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