Stochastic nature of earthquake has raised a challenge for engineers to choose which record for their analyses. Clustering is offered as a solution for such a data mining problem to automatically distinguish between ground motion records based on similarities in the corresponding seismic attributes. The present work formulates an optimization problem to seek for the best clustering measures. In...

The DBSCALE [1] algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. Such metaheuristic algorithms include Ant Colony Optimization Algorithms, Particle Swarm Optimizations and Genetic Algorithm has received increasing attention in recent years. Ant Colony Optimization (ACO) is a technique that was introduce...

in this research, the framework is presented for unsupervised change detection using multitemporal sar images based on integration clustering and level set methods. spatial correlation between pixels were considered by using contextual information. also as proposed method was used integration of gustafson-kessel clustering techniques (gkc) and level set methods for change detection. using clust...

this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...

This paper proposes a clustering method SOMAK, which is composed by Self-Organizing Maps (SOM) followed by the Ant K-means (AK) algorithm. The aim of this method is not to find an optimal clustering for the data, but to obtain a view about the structure of data clusters. SOM is an Artificial Neural Network, which has one of its characteristics the nonlinear projection. AK is a meta-heuristic ap...

Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based b...

Various techniques exist to solve the non-convex optimization problem of clustering. Recent developments have employed a deterministic annealing approach to solving this problem. In this letter a new approximation clustering algorithm, incorporating a gradient descent technique with deterministic annealing, is described. Results are presented for this new method, and its performance is compared...

Finding the optimal number of clusters has remained to be a challenging problem in data mining research community. Several approaches have been suggested which include evolutionary computation techniques like genetic algorithm, particle swarm optimization, differential evolution etc. for addressing this issue. Many variants of the hybridization of these approaches also have been tried by resear...

The gist of the paper is to provide an insight about the various clustering using bio-inspired optimization for Protein Interaction Network. The major idea behind the clustering protein-protein interaction network is to identify dense sub-graphs that show significant functional modules in protein-protein interactions. A set of proteins that interact with each other are actors of a specific cell...