An On-line Density-based Clustering Algorithm for Spatial Data Stream

2012 ◽  
Vol 38 (6) ◽  
pp. 1051 ◽  
Author(s):  
Yan-Wei YU ◽  
Qin WANG ◽  
Jun KUANG ◽  
Jie HE
Keyword(s):  
Spatial Data ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Line Density ◽  
Density Based Clustering ◽  
On Line ◽  
Spatial Data Stream

DBSCANI: Noise-Resistant Method for Missing Value Imputation

2016 ◽  
Vol 25 (3) ◽  
pp. 431-440 ◽  
Author(s):  
Archana Purwar ◽  
Sandeep Kumar Singh
Keyword(s):  
Spatial Data ◽  
Missing Values ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Data Sets ◽  
Quality Of Data ◽  
Data Set ◽  
Dbscan Clustering ◽  
Density Based Clustering

AbstractThe quality of data is an important task in the data mining. The validity of mining algorithms is reduced if data is not of good quality. The quality of data can be assessed in terms of missing values (MV) as well as noise present in the data set. Various imputation techniques have been studied in MV study, but little attention has been given on noise in earlier work. Moreover, to the best of knowledge, no one has used density-based spatial clustering of applications with noise (DBSCAN) clustering for MV imputation. This paper proposes a novel technique density-based imputation (DBSCANI) built on density-based clustering to deal with incomplete values in the presence of noise. Density-based clustering algorithm proposed by Kriegal groups the objects according to their density in spatial data bases. The high-density regions are known as clusters, and the low-density regions refer to the noise objects in the data set. A lot of experiments have been performed on the Iris data set from life science domain and Jain’s (2D) data set from shape data sets. The performance of the proposed method is evaluated using root mean square error (RMSE) as well as it is compared with existing K-means imputation (KMI). Results show that our method is more noise resistant than KMI on data sets used under study.

IMPROVED DENSITY BASED ALGORITHM FOR DATA STREAM CLUSTERING

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Maryam Mousavi ◽  
Azuraliza Abu Bakar
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Local Density ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Stream Clustering ◽  
Density Based Clustering ◽  
Clustering Quality ◽  
Data Stream Clustering

In recent years, clustering methods have attracted more attention in analysing and monitoring data streams. Density-based techniques are the remarkable category of clustering techniques that are able to detect the clusters with arbitrary shapes and noises. However, finding the clusters with local density varieties is a difficult task. For handling this problem, in this paper, a new density-based clustering algorithm for data streams is proposed. This algorithm can improve the offline phase of density-based algorithm based on MinPts parameter. The experimental results show that the proposed technique can improve the clustering quality in data streams with different densities.

Unbounded Spatial Data Stream Query Processing using Spatial Semijoins

2021 ◽  
Vol 15 (02) ◽  
pp. 33-41
Author(s):  
Wendy Osborn
Keyword(s):  
Query Processing ◽  
Data Streams ◽  
Spatial Data ◽  
Data Stream ◽  
Spatial Extent ◽  
Common Region ◽  
Data Set ◽  
Spatial Join ◽  
The Common ◽  
Spatial Data Stream

In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.

On-Line Identification of Takagi-Sugeno Model Based on Improved Density-Based Clustering Algorithm

2012 ◽  
Vol 6 (1) ◽  
pp. 498-501
Author(s):  
Duan Yubo ◽  
Chen Qin ◽  
Shao Keyong ◽  
Shang Yintong ◽  
Bai Ting
Keyword(s):  
Clustering Algorithm ◽  
Model Based ◽  
Line Identification ◽  
Density Based Clustering ◽  
On Line ◽  
Sugeno Model ◽  
Takagi Sugeno
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