Classifying and clustering in negative databases

Authors:
Ran Liu;Wenjian Luo;Lihua Yue
Affiliations:
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 230027 and Anhui Province Key Laboratory of Software Engineering in Computing and Communicati ...;School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 230027 and Anhui Province Key Laboratory of Software Engineering in Computing and Communicati ...;School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 230027 and Anhui Province Key Laboratory of Software Engineering in Computing and Communicati ...
Venue:
Frontiers of Computer Science: Selected Publications from Chinese Universities
Year:
2013

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Abstract

Recently, negative databases (NDBs) are proposed for privacy protection. Similar to the traditional databases, some basic operations could be conducted over the NDBs, such as select, intersection, update, delete and so on. However, both classifying and clustering in negative databases have not yet been studied. Therefore, two algorithms, i.e., a k nearest neighbor (kNN) classification algorithm and a k-means clustering algorithm in NDBs, are proposed in this paper, respectively. The core of these two algorithms is a novelmethod for estimating the Hamming distance between a binary string and an NDB. Experimental results demonstrate that classifying and clustering in NDBs are promising.