科研之友
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摘要
Having high accuracy results in data clustering and preserving the privacy of data are among the main challenges in privacy preserving clustering (PPC) techniques. High dimensionality of data is another challenge in PPC, which reduces the efficiency of the data mining algorithms. Therefore, PPC algorithms are divided into two categories. The algorithms in the first category protect the data privacy and do not reduce the data dimensionality whereas the algorithms in the second category not only preserve the data privacy but also reduce the data dimensionality. The techniques based on geometric data transformation methods (GTDMs) are related to the first category whereas the techniques based on random projection (RP), discrete cosine transform (DCT) and Haar wavelet transform (HWT) are related to the second category. The GTDMs algorithms do not reduce the data dimensionality. This is the main drawback of this algorithm which causes reduction in the performance of data mining algorithm in large datasets. The technique based on Haar wavelet transform automatically recognizes the dimensionality of the transformed data by using data energy. However, the main problem is the nature of Haar wavelet, which has one vanishing point. In this paper, we show that using Daubechies-2 wavelet, which has two vanishing points, increases the clustering quality. Therefore, to fix the drawback of the PPC algorithm based on Haar wavelet, we introduce a new algorithm to improve both the clustering quality and the privacy measure of data by using Daubechies-2 wavelet transform (D2WT). The results of experiments using several datasets, comparing the new algorithm with other existing techniques, are also presented in this paper.
- 出版日期2013-9
