FUZZY GRAVITATIONAL SEARCH ALGORITHM AN APPROACH FOR DATA MINING
author
Abstract:
The concept of intelligently controlling the search process of gravitational search algorithm (GSA) is introduced to develop a novel data mining technique. The proposed method is called fuzzy GSA miner (FGSA-miner). At first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major roles on search process of GSA. Then the improved GSA (namely Fuzzy-GSA) is employed to construct a novel data mining algorithm for classification rule discovery from reference data sets. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the powerfulness of the proposed method. The comparative results illustrate that performance of the proposed FGSA-miner considerably outperforms the standard GSA. Also it is shown that the performance of the FGSA-miner is comparable to, sometimes better than those of the CN2 (a traditional data mining method) and similar approach which have been designed based on other swarm intelligence algorithms (ant colony optimization and particle swarm optimization) and evolutionary algorithm (genetic algorithm).
similar resources
fuzzy gravitational search algorithm an approach for data mining
the concept of intelligently controlling the search process of gravitational search algorithm (gsa) is introduced to develop a novel data mining technique. the proposed method is called fuzzy gsa miner (fgsa-miner). at first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major ro...
full textAn improved opposition-based Crow Search Algorithm for Data Clustering
Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CS...
full texta swift heuristic algorithm base on data mining approach for the Periodic Vehicle Routing Problem: data mining approach
periodic vehicle routing problem focuses on establishing a plan of visits to clients over a given time horizon so as to satisfy some service level while optimizing the routes used in each time period. This paper presents a new effective heuristic algorithm based on data mining tools for periodic vehicle routing problem (PVRP). The related results of proposed algorithm are compared with the resu...
full textAn Integrated DEA and Data Mining Approach for Performance Assessment
This paper presents a data envelopment analysis (DEA) model combined with Bootstrapping to assess performance of one of the Data mining Algorithms. We applied a two-step process for performance productivity analysis of insurance branches within a case study. First, using a DEA model, the study analyzes the productivity of eighteen decision-making units (DMUs). Using a Malmquist index, DEA deter...
full textAN OPTIMIZED NEURO-FUZZY GROUP METHOD OF DATA HANDLING SYSTEM BASED ON GRAVITATIONAL SEARCH ALGORITHM FOR EVALUATION OF LATERAL GROUND DISPLACEMENTS
During an earthquake, significant damage can result due to instability of the soil in the area affected by internal seismic waves. A liquefaction-induced lateral ground displacement has been a very damaging type of ground failure during past strong earthquakes. In this study, neuro-fuzzy group method of data handling (NF-GMDH) is utilized for assessment of lateral displacement in both ground sl...
full textAn Improved Algorithm for Fuzzy Data Mining for Intrusion Detection
We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection. In this paper we describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency. We use sets of fuzzy association rules that are mined from network audit data as models of " normal behavior. " To detect anoma...
full textMy Resources
Journal title
volume 9 issue 1
pages 21- 37
publication date 2012-02-10
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023