Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

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 betwee...

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Vydáno v:Mathematical Problems in Engineering Ročník 2016; číslo 2016; s. 734 - 742
Hlavní autoři: Sharma, Arvind, Tiwari, Akhilesh, Gupta, R. K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Limiteds 01.01.2016
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Témata:
Air traffic controllers
Algorithms
Clustering
Data collection
Data mining
Datasets
Density
Design
Geographic information systems
Global Positioning System
Knowledge discovery
Medical imaging
Methods
Noise
Noise generation
Noise prediction
Remote sensing
Satellite imagery
Satellite navigation systems
Spatial data
Water treatment
Weather forecasting
ISSN:1024-123X, 1563-5147
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Popis
Shrnutí: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.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2016/1564516