Mean shift is a nonparametric clustering technique that does not require the number of clusters in input and can find clusters of arbitrary shapes. While appealing, the performance of the mean shift algorithm is sensitive to the selection of the bandwidth, and can fail to capture the correct clustering structure when multiple modes exist in one cluster. DBSCAN is an efficient density based clus...

The mean shift clustering algorithm is a useful tool for clustering numeric data. Recently, Chang-Chien et al. [1] proposed a mean shift clustering algorithm for circular data that are directional data on a plane. In this paper, we extend the mean shift clustering for directional data on a hypersphere. The three types of mean shift procedures are considered. With the proposed mean shift cluster...

in recent years stock exchange has become one of the most attractive and growing businesses in respect of investment and profitability. but applying a scientific approach in this field is really troublesome because of variety and complexity of decision making factors in the field. this paper tries to deliver a new solution for portfolio selection based on multi criteria decision making literatu...

ontology is the main infrastructure of the semantic web which provides facilities for integration, searching and sharing of information on the web. development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. by emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory con...

A new method for image clustering with density maps derived from Self-Organizing Maps (SOM) is proposed together with a clarification of learning processes during a construction of clusters. Simulation studies and the experiments with remote sensing satellite derived imagery data are conducted. It is found that the proposed SOM based image clustering method shows much better clustered result fo...