DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Mining distributed databases is emerging as a fundamental computational problem. A common approach for mining distributed databases is to move all of the data from each database to a central site and a single model is built. Privacy concerns in many application domains prevents sharing of data, which limits data mining technology to identify patterns and trends from large amount of data. Traditional data mining algorithms have been developed within a centralized model. However, distributed knowledge discovery has been proposed by many researchers as a solution to privacy preserving data mining techniques. By vertically partitioned data, each site contains some attributes of the entities in the environment.In this paper, we present a method for Agglomerative clustering algorithm in situations where different sites contain different attributes for a common set of entities for verticallypartitioned data. Using association rules data are partitioned into vertically.
