The Lorenz curve is a fundamental tool for analyzing income and wealth distribution inequality. Indeed, the its derivative, so-called share density, provide valuable information regarding There widely recognized connection between elements from theory field. Starting this evidence, aim of work to compare inequality different subgroups, by using proper dissimilarity measure, borrowed theory, par...

As a relatively novel density-based clustering algorithm, Density peak (DPC) has been widely studied in recent years. DPC sorts all points descending order of local density and finds neighbors for each point turn to assign the appropriate clusters. The algorithm is simple effective but some limitations applicable scenarios. If difference between clusters large or data distribution nested struct...

The k_means clustering algorithm has very extensive application. paper gives out_in based on density . combines distance with data to adapt distribution. It can effectively solve the of data. Out_in reduce distorition by move out and in. Simulation results show that is more effective than algorithm.

Geographical flows reflect the movements, spatial interactions or connections among locations and are generally abstracted as origin-destination (OD) flows. In this context, clustering is a pattern describing group of with adjacent O D points. For data composed two types (bivariate-flow data), bivariate-flow cluster comprising flows, at least one which exhibits pattern. cluster, varying flow de...

Centroid based clustering approaches, such as k-means, are relatively fast but inaccurate for arbitrary shape clusters. Fuzzy c-means with Mahalanobis distance can accurately identify clusters if data set be modelled by a mixture of Gaussian distributions. However, they require number apriori and bad initialization cause poor results. Density methods, DBSCAN, overcome these disadvantages. may p...

Many clustering algorithms suffer from scalability problems on massive datasets and do not support any user interaction during runtime. To tackle these problems, anytime clustering algorithms are proposed. They produce a fast approximate result which is continuously refined during the further run. Also, they can be stopped or suspended anytime and provide an answer. In this paper, we propose a ...