Abstract
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.
Highlights
There are so many techniques and algorithms to discover meaningful and useful results from a large amount of spatial databases [1]
Spatial database contains different objects with similar attributes and properties and these properties are responsible for grouping of similar types of objects in a group which is the basis of clustering
COD-CLARANS (Clustering with Obstructed Distance based on CLARANS) is the first clustering algorithm that solves a problem which is known as the problem of clustering with obstacles entities (COE)
Summary
There are so many techniques and algorithms to discover meaningful and useful results from a large amount of spatial databases [1]. Clustering is one of the major data mining methods for spatial databases. As discussed above our main objective is to design improved DBSCAN algorithm [3] for spatial data sets. This paper is basically designed to give a complete working process of spatial data mining with new ideas of improvement in respect of drawbacks of previous work, that is, DBSCAN. For multidimensional data [5], moving objects and dynamic data selection needs new and advance methods of mining and knowledge discovery. To handle such kind of challenges and research activities, spatial data mining has developed as strong tool with geovisualization concept. In point 8 conclusion and future scope is discussed
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