Add to ORKGAbstract: The aim of this paper is to group territorial units in areas of high intensity, using SaTScan and Seg-DBSCAN clustering methods to aggregate adjacent spatial units that are homogeneous with respect to the phenomenon being studied. SaTScan scans the region of interest with a moving window and compares a smoothing of the intensity inside and outside it so that units belonging to contiguous windows with similar intensity are aggregated into a cluster. On the other hand, Seg-DBSCAN, a new version of DBSCAN, limits the arbitrariness of the choice of input parameters and identifies clusters as dense regions in space. As an application we analyze geo-referenced data concerning housing problems in Bari and we propose a comparison between ...
Abstract: Clustering plays an outstanding role in data mining applications such as scientific data e...
Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The ...
The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a pioneering...
The aim of this paper is to group territorial units in areas of high intensity, using SaTScan and S...
The aim of this paper is to identify territorial areas and/or population subgroups characterized by ...
Clustering algorithms are attractive for the task of class iden-tification in spatial databases. How...
The presence of a varied range of definitions on the theme of poverty underlines the necessity of no...
Clustering algorithms are data attractive for the last class identification in spatial databases. Th...
In our time people and devices constantly generate data. User activity generates data about needs an...
The rapid developments in the availability and access to spatially referenced information in a varie...
The spatial scan statistic method has been widely used for detecting disease clusters. Its results m...
Regionalisation, a prominent problem from social geography, could be solved by a classification algo...
The aim of this paper is to compare two different clustering methods We consider DBSCAN before in t...
Abstract- Clustering is the process of organizing similar objects into the same clusters and dissimi...
There are many techniques available in the field of data mining and its subfield spatial data mining...
Abstract: Clustering plays an outstanding role in data mining applications such as scientific data e...
Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The ...
The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a pioneering...
The aim of this paper is to group territorial units in areas of high intensity, using SaTScan and S...
The aim of this paper is to identify territorial areas and/or population subgroups characterized by ...
Clustering algorithms are attractive for the task of class iden-tification in spatial databases. How...
The presence of a varied range of definitions on the theme of poverty underlines the necessity of no...
Clustering algorithms are data attractive for the last class identification in spatial databases. Th...
In our time people and devices constantly generate data. User activity generates data about needs an...
The rapid developments in the availability and access to spatially referenced information in a varie...
The spatial scan statistic method has been widely used for detecting disease clusters. Its results m...
Regionalisation, a prominent problem from social geography, could be solved by a classification algo...
The aim of this paper is to compare two different clustering methods We consider DBSCAN before in t...
Abstract- Clustering is the process of organizing similar objects into the same clusters and dissimi...
There are many techniques available in the field of data mining and its subfield spatial data mining...
Abstract: Clustering plays an outstanding role in data mining applications such as scientific data e...
Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The ...
The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a pioneering...