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Individual privacy preservation has recently become an increasingly important issue when publishing microdata for mining purpose. K-anonymity is a popular model for protecting privacy, which requires that each record in the released dataset be indistinguishable with at least (k-1) other records with respect to quasi-identifier. MDAV, an efficient k-anonymization algorithm, has been extensively investigated...
Clustering is the process of discovering groups within multidimensional data, based on similarities, with a minimal knowledge of their structure. In previous works, we presented an algorithm (partSOM) to cluster distributed datasets, based on self-organizing maps (SOM). This work extends this approach presenting a strategy for efficient cluster analysis in distributed databases using SOM and K-means...
The basic concept of clustering and its correlating research work is firstly present, a new algorithm based on least clustering cell (LCC) is proposed and analyzed which concerns the advantages and disadvantage of k-means and grid clustering algorithm. This algorithm is efficient in dealing with huge amounts of data and can make paralleled processing, which is proved to be correct, efficient and fast...
In this paper, an attempt has been made to explore the effect of frequency of co-occurrence of features on the accuracy of the clustering results. This has been achieved by incorporating the frequency component in the clustering algorithm. The frequency, we mean here is the number of times the sequence of features appear in the data set. We try to utilize this component in the algorithm and study...
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