The comprehensive expression of indoor maps directly affects the visualization effect map and user’s reading experience. Currently, only points, lines, polygons outdoor are used as objects cartographic generalization. Therefore, this study considers area features generalization deems automatic clustering shopping malls research goal. approach is to construct an encoder-decoder model, where enco...

Abstract The density peaks clustering (DPC) algorithm is a classical and widely used method. However, the DPC requires manual selection of cluster centers, single way calculation, cannot effectively handle low-density points. To address above issues, we propose novel deviation multi-peaks automatic method (AmDPC) in this paper. Firstly, new local-density use to measure relationship between data...

one of the most important issues in urban planning is developing sustainable public transportation. the basic condition for this purpose is analyzing current condition especially based on data. data mining is a set of new techniques that are beyond statistical data analyzing. clustering techniques is a subset of it that one of it’s techniques used for analyzing passengers’ trip. the result of t...

Background: The design of intensity modulated radiation therapy (IMRT) plans is difficult and time-consuming. The retrieval of similar IMRT plans from the IMRT plan dataset can effectively improve the quality and efficiency of IMRT plans and automate the design of IMRT planning. However, the large IMRT plans datasets will bring inefficient retrieval result. Materials and Methods: An intensity-m...

This paper describes an automatic target recognition system for detecting targets in temporal sequences of intensity LADAR images. The system rst nds all objects in the images using a general method that is capable of nding both blobs and curves. Then features of the objects are extracted. Next fuzzy c-means is used to cluster the objects in feature space. Finally, prototypes generated for each...

Cluster analysis deals with the automatic discovery of the grouping of a set of patterns. Despite more than 40 years of research, there are still many challenges in data clustering from both theoretical and practical viewpoints. In this paper, we describe several recent advances in data clustering: clustering ensemble, feature selection, and clustering with constraints.