Processing noise data is one of the most important fields on mining data streams. To address this problem, we consider a Density Based Spatial Clustering of Application with Noise (DBSCAN) algorithm, which takes advantage of filtering noise data to handle noise data in data streams. Many experiments show that DBSCAN algorithm will cost a lot of time when the database is large. Therefore we impr...

Data clustering plays an important role in various fields. Data clustering approaches have been presented in recent decades. Identifying clusters with widely differing shapes, sizes and densities in the presence of noise and outliers is challenging. Many density-based clustering algorithms, such as DBSCAN, can locate arbitrary shapes, sizes and filter noise, but cannot identify clusters based o...

Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it fails to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant dens...

Clustering, as a kind of data mining methods, with the characteristic of no supervising, quick modeling is widely used in intrusion detection. However, most of the traditional clustering algorithms use a single data point as a calculating unit, and the drawback exists in time wasting to calculate one data point after another when clustering, meanwhile, a single local change of data will signifi...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Spatial data clustering is one of the important data mining techniques for extracting knowledge from large amount of spatial data collected in various applications, such as remote sensing, GIS, computer cartography, environmental assessment and pla...

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algo...

Cluster analysis has an important role in analysis of the ElectroEnsepholoGraphy (EEG) signals of the brain activities [Escalona-Moran et al., 2007; Jin S-H. Et al., 2005; Van Hese et al., 2008]. The primary objective of clustering is to simplify statistical analysis by grouping similar objects in a cluster. Clustering methods can be divided into five main groups such as hierarchical, prototype...

As a research branch of data mining, clustering, as an unsupervised learning scheme, focuses on assigning objects in the dataset into several groups, called clusters, without any prior knowledge. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most widely used clustering algorithms for spatial datasets, which can detect any shapes of clusters and can automatic...

Density-based clustering algorithms such as DBSCAN have been widely used for spatial knowledge discovery as they offer several key advantages compared to other clustering algorithms. They can discover clusters with arbitrary shapes, are robust to noise and do not require prior knowledge (or estimation) of the number of clusters. The idea of using a scan circle centered at each point with a sear...

Density-based clustering algorithms such as DBSCAN have been widely used for spatial knowledge discovery as they offer several key advantages compared to other clustering algorithms. They can discover clusters with arbitrary shapes, are robust to noise and do not require prior knowledge (or estimation) of the number of clusters. The idea of using a scan circle centered at each point with a sear...