Classifying and clustering in negative databases

doi:10.1007/s11704-013-2318-9

Classifying and clustering in negative databases

2013-09-25

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DOI：10.1007/s11704-013-2318-9

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Abstract

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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.

Keywords:

negative databases

classification

clustering

k nearest neighbor

k-means

hamming distance

Classifying and clustering in negative databases

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