![](/images/graphics-bg.png)
Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data
Joint Authors
Sharma, Arvind
Gupta, R. K.
Tiwari, Akhilesh
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-30
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects.
Data objects related with spatial features are called spatial databases.
These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons.
A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system.
To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper.
This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process.
In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process.
Clustering methods are useful in various fields of human life such as GIS (Geographic Information System), GPS (Global Positioning System), weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing.
This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise).
The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.
American Psychological Association (APA)
Sharma, Arvind& Gupta, R. K.& Tiwari, Akhilesh. 2016. Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1111743
Modern Language Association (MLA)
Sharma, Arvind…[et al.]. Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data. Mathematical Problems in Engineering No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1111743
American Medical Association (AMA)
Sharma, Arvind& Gupta, R. K.& Tiwari, Akhilesh. Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1111743
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111743