The problem of detecting clusters of points belonging to a spatial point process arises in many applications. In this paper , we introduce the new clustering algorithm DBCLASD (Distribution Based Clustering of LArge Spatial Databases) to discover clusters of this type. The results of experiments demonstrate that DBCLASD, contrary to partitioning algorithms such as CLARANS, discovers clusters of...

In this work we introduce an anisotropic density-based clustering algorithm. It outperforms DBSCAN and OPTICS for the detection of anisotropic spatial point patterns and performs equally well in cases that do not explicitly benefit from an anisotropic perspective. ADCN has the same time complexity as DBSCAN and OPTICS, namely O(n log n) when using a spatial index, O(n2) otherwise. STKO@Geograph...

As a kind of stream data mining method, stream clustering has great potentiality in areas such as network traffic analysis, intrusion detection, etc. This paper proposes a novel grid-based clustering algorithm for stream data, which has both advantages of grid mapping and DBSCAN algorithm. The algorithm adopts the two-phase model and in the online phase, it maps stream data into a grid and the ...

DETECTION OF OUTLIERS IN TIME SERIES DATA Samson Kiware, B.A. Marquette University, 2010 This thesis presents the detection of time series outliers. The data set used in this work is provided by the GasDay Project at Marquette University, which produces mathematical models to predict the consumption of natural gas for Local Distribution Companies (LDCs). Flow with no outliers is required to dev...

In this paper we address the issue of privacy preserving clustering. Specially, we consider a scenario in which two parties owning confidential databases wish to run a clustering algorithm on the union of their databases, without revealing any unnecessary information. This problem is a specific example of secure multi-party computation and as such, can be solved using known generic protocols. H...

Clustering is the process of grouping similar data into clusters and dissimilar data into different clusters. Density-based clustering is a useful clustering approach such as DBSCAN and OPTICS. The increasing volume of data and varying size of data sets lead the clustering process challenging. So that we propose a parallel framework of clustering with advanced approach called MapReduce. We deve...

Abstract As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial of applications with noise (DBSCAN), as a common can achieve clusters via finding high-density areas separated by low-density based on cluster density. Different from other methods, DBSCAN work well for any shape the database and effectively exceptional data. However,...

Density based clustering algorithm is the primary methods for clustering in data mining. The clusters which are formed based on the density are easy to understand. It does not limit itself to the shapes of clusters. This paper gives a survey of the existing density based algorithms namely DBSCAN, VDBSCAN, DVBSCAN, ST-DBSCAN and DBCLASD based on the essential parameters needed for a good cluster...

Spatial data mining is the task of discovering knowledge from spatial data. Density-Based Spatial Clustering occupies an important position in spatial data mining task. This paper presents a detailed survey of density-based spatial clustering of data. The various algorithms are described based on DBSCAN comparing them on the basis of various attributes and different pitfalls. The advantages and...