This site uses cookies to deliver our services and to ensure you get the best experience. By continuing to use this site, you consent to our use of cookies and acknowledge that you have read and understand our Privacy Policy, Cookie Policy, and Terms
In spatial clustering, the scale of spatial data is usually very large. Spatial clustering algorithms need high performance, good scalability, and are able to deal with noise and ...
The spatial clustering of genes across different genomes has been used to study important problems in comparative genomics, from identification of operons to detection of homologo...
Spatial clustering is an important topic in knowledge discovery research. However, most clustering methods do not consider semantic information during the clustering process. In th...
Spatial clustering is an active research area in spatial data mining with various methods reported. In this paper, we compare two density-based methods, DBSCAN and DBRS. First, we ...
Spatial clustering, which groups similar spatial objects into classes, is an important research topic in spatial data mining. Many spatial clustering methods have been developed re...
This paper proposes a new principle for designing MAC protocols for spread spectrum based ad hoc networks ? inducing spatial clustering in contending transmitters/receivers. We fi...
Abstract. Sensor networks monitor physical phenomena over large geographic regions. Scientists can gain valuable insight into these phenomena, if they understand the underlying dat...
The combination of fully sequence genomes and new technologies for high density arrays and ultra-rapid sequencing enables the mapping of generegulatory and epigenetics marks on a g...