<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Many real world problems have big data, including recorded fields and/or attributes. In such cases, data mining requires dimension reduction techniques because there are serious challenges facing conventional clustering methods in dealing with big data. The subspace selection method is one of the most important dimension reduction techniques. In such methods, a selected set of subspaces is substituted for the general dataset of the problem and clustering is done using this set. This article introduces the Shared Subscribe Hyper Simulation Optimization (SUBHSO) algorithm to introduce the optimized cluster centres to a set of subspaces. SUBHSO uses an optimization loop for modifying and optimizing the coordinates of the cluster centres with the particle swarm optimization (PSO) and the fitness function calculation using the Monte Carlo simulation. The case study on the big data of Iran electricity market (IEM) has shown the improvement of the defined fitness function, which represents the cluster cohesion and separation relative to other dimension reduction algorithms.</p></abstract>

Many real world problems have big data, including recorded fields and/or attributes. In such cases, data mining requires dimension reduction techniques because there are serious challenges facing conventional clustering methods in dealing with big data. The subspace selection method is one of the most important dimension reduction techniques. In such methods, a selected set of subspaces is substituted for the general dataset of the problem and clustering is done using this set. This article introduces the Shared Subscribe Hyper Simulation Optimization (SUBHSO) algorithm to introduce the optimized cluster centres to a set of subspaces. SUBHSO uses an optimization loop for modifying and optimizing the coordinates of the cluster centres with the particle swarm optimization (PSO) and the fitness function calculation using the Monte Carlo simulation. The case study on the big data of Iran electricity market (IEM) has shown the improvement of the defined fitness function, which represents the cluster cohesion and separation relative to other dimension reduction algorithms.

Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data – Using Big Databases of Iran Electricity Market

Department of Industrial Engineering, Central Tehran Branch

Department of Industrial Engineering, Science and Research Branch

Department of Electrical Engineering, Abbaspour School of Engineering

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Many real world problems have big data, including recorded fields and/or attributes. In such cases, data mining requires dimension reduction techniques because...

Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data – Using Big Databases of Iran Electricity Market

Publicado en línea: 20 jun 2019

Páginas: 49 - 60

DOI: https://doi.org/10.2478/acss-2019-0007

Palabras clave
Clustering, electricity demand forecasting, Monte Carlo simulation, particle swarm optimization, subspace search method

© 2019 Mesbaholdin Salami et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data – Using Big Databases of Iran Electricity Market

Publicado en línea: 20 jun 2019

Páginas: 49 - 60

DOI: https://doi.org/10.2478/acss-2019-0007

Palabras claveClustering, electricity demand forecasting, Monte Carlo simulation, particle swarm optimization, subspace search method

© 2019 Mesbaholdin Salami et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Palabras clave
Clustering, electricity demand forecasting, Monte Carlo simulation, particle swarm optimization, subspace search method