Subspace clustering is an important unsupervised approach. It based on the assumption that high-dimensional data points are approximately distributed around several low-dimensional linear subspaces. The majority of prominent subspace algorithms rely representation as combinations other points, which known a self-expressive representation. To overcome restrictive linearity assumption, numerous n...

This paper presents a comparative study between three versions of adaptive neuro-fuzzy inference system (ANFIS) algorithms and a pseudo-forward equation (PFE) to characterize the North Sea reservoir (F3 block) based on seismic data. According to the statistical studies, four attributes (energy, envelope, spectral decomposition and similarity) are known to be useful as fundamental attributes in ...

The Wisdom of Crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. This theory used for in clustering problems. Previous researches showed that this theory can significantly increase the stability and performance of...

Clustering is grouping input data sets into subsets, called ’clusters’ within which the elements are somewhat similar. In general, clustering is an unsupervised learning task as very little or no prior knowledge is given except the input data sets. The tasks have been used in many fields and therefore various clustering algorithms have been developed. Clustering task is, however, computationall...

Graph clustering algorithms are Random walk and minimum spanning tree algorithms. Random walk has been used to identify significant vertices in the graph that receive maximum flow while minimum spanning tree algorithm has been used to identify significant edges in the graph .We believe these two graph algorithms have useful applications in clustering, namely for identifying centroids and for id...

fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. in this research, fcm is chosen for fuzzy clustering. parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. these two parameters require tuning to reduce the overfitting in the...

The Wisdom of Crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. This theory used for in clustering problems. Previous researches showed that this theory can significantly increase the stability and performance of...

As with most computer science problems, representation of the data is the key to ecient and eective solutions. Piecewise linear representation has been used for the representation of the data. This representation has been used by various researchers to support clustering, classication, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain...