AIRCC's International Journal of Computer Science and Information Technology

Ch. N. Santhosh Kumar ¹, V. Sitha Ramulu ¹, K. Sudheer Reddy ², Suresh Kotha ¹, Ch. Mohan Kumar ¹

Affiliations
1 Dept. of CSE, Swarna Bharathi Inst. of Sc. & Tech, Khammam, A.P., IN
2 Dept. of CSE, Acharya Nagarjuna University, Guntur, A.P., IN

Abstract

Data mining, which is refers to as Knowledge Discovery in Databases(KDD), means a process of nontrivial exaction of implicit, previously useful and unknown information such as knowledge rules, descriptions, regularities, and major trends from large databases. Data mining is evolved in a multidisciplinary field , including database technology, machine learning, artificial intelligence, neural network, information retrieval, and so on. In principle data mining should be applicable to the different kind of data and databases used in many different applications, including relational databases, transactional databases, data warehouses, object-oriented databases, and special application-oriented databases such as spatial databases, temporal databases, multimedia databases, and time-series databases. Spatial data mining, also called spatial mining, is data mining as applied to the spatial data or spatial databases. Spatial data are the data that have spatial or location component, and they show the information, which is more complex than classical data. A spatial database stores spatial data represents by spatial data types and spatial relationships and among data. Spatial data mining encompasses various tasks. These include spatial classification, spatial association rule mining, spatial clustering, characteristic rules, discriminant rules, trend detection. This paper presents how spatial data mining is achieved using clustering.

Keywords

Clustering, Database, Data Mining, Spatial Data.

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