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• W1593779549 abstract "Clustering is the process of discovering groups within high-dimensional databases, based on similarities, with a minimal knowledge of their structure. Traditional clustering algorithms perform it over centralized databases, however, recent applications require datasets distributed among several sites. Therefore, in distributed database environments, all distributed data must be concentrated on a central site before applying traditional algorithms. There is a series of limitations which hinder the utilization of traditional data mining techniques on distributed databases. The approach commonly taken, the gathering of all distributed databases in a central unit, followed by algorithm application, is strongly criticized, because in these cases, it is important to take into consideration some issues, namely: the possibility of existence of similar data with different names and formats, differences in data structures, and conflicts between one and another database (Zhang et al., 2003). Besides, the unification of all of the registers in a single database may take to the loss of meaningful information, once that statistically interesting values in a local context may be ignored when gathered to other ones in a larger volume. On the other hand, integration of several database in a single location is not suggested when it is composed of very large databases. If a great organization has large disperse databases and needs to gather all the data in order to apply on them data mining algorithms, this process may demand great data transference, which may be slow and costly (Forman & Zhang, 2000). Moreover, any change that may occur in distributed data, for instance inclusion of new information or alteration of those already existing will have to be updated along with the central database. This requires a very complex data updating strategy, with overload of information transference in the system. Furthermore, in some domains such as medical and business areas whereas distributed databases occurs, transferring raw datasets among parties can be insecure because confidential information can be obtained, putting in risk privacy preserving and security requirements. Due to all of these problems related to database integration, research for algorithms that perform data mining in a distributed way is not recent. In the end of the 90s, several researches about algorithms to effectuate distributed data mining started to appear, having been strengthened mainly by the rise of the distributed database managing systems and of the need for an analysis of such data in the way that they were dispersed (DeWitt & Gray, 1992; Souza, 1998). Currently, there is an increasing demand for methods with the ability to" @default.
• W1593779549 created "2016-06-24" @default.
• W1593779549 creator A5005185037 @default.
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• W1593779549 date "2011-01-21" @default.
• W1593779549 modified "2023-10-17" @default.
• W1593779549 title "Privacy-Preserving Clustering on Distributed Databases: A Review and Some Contributions" @default.
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• W1593779549 doi "https://doi.org/10.5772/13945" @default.
• W1593779549 hasPublicationYear "2011" @default.
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