A Secure and Optimal Data Clustering Technique over Distributed Networks
| Autor: | S. Jayaprada, M. Yogita Bala |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Community based
Computer science Automatic learning computer.software_genre Privacy preserving Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION Information engineering Data stream clustering Factor (programming language) Data mining Cluster analysis computer computer.programming_language |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319137278 |
| DOI: | 10.1007/978-3-319-13728-5_19 |
| Popis: | Clustering is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. The goal is to create clusters that are coherent internally, but substantially different from each other. Privacy is an important factor while datasets or data integrates from different data holders for mining over a distributed networks. Secured and optimal data clustering in distributed networks has played an important role in many fields like Information Retrieval, Data mining, Knowledge and Data engineering or community based clustering. Secured mining of data is required in open network. In this paper we are proposing an efficient privacy preserving and optimal data clustering technique over distributed networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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