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Microaggregation is a commonly used technique for statistical disclosure control of microdata. It divides the microdata into groups such that each group contains no fewer than k records, where k is a user-specified parameter; then it replaces each group with the group's centroid. The problem underlying microaggrgation is called the k-Partitions problem. The k-Partitions problem is a constrained optimization...
This study develops the kernel intuitionistic fuzzy c-means clustering (KIFCM), and applies KIFCM in E-learning customer analysis. KIFCM combines intuitionistic fuzzy sets (IFSs) with kernel fuzzy c-means clustering (KFCM). The KIFCM has advantages of IFSs and KFCM which can effectively handle uncertain data and simultaneously map data to kernel space. The proposed KFCM has better performance than...
Power consumption is an increasingly impressing concern for data servers as it directly affects running costs and system reliability. Prior studies have shown that most memory space on data servers is used for buffer caching and thus cache replacement becomes critical. Two conflicting factors of buffer caching impacts memory energy efficiency: (1) a higher hit rate reduces memory traffic and thus...
Power consumption is an increasingly impressing concern for data servers as it directly affects running costs and system reliability. Prior studies have shown most memory space on data servers are used for buffer caching and thus cache replacement becomes critical. Temporally concentrating memory accesses to a smaller set of memory chips increases the chances of free riding through DMA overlapping...
To select a minimal and effective subset from a mass of bands is the key issue of the study on hyperspectral image classification. This paper put forwards a novel band selection algorithm, which combines mutual information-based grouping method and genetic algorithm. The proposed algorithm reduces the computation cost significantly, as well as keeps a better precision. In addition, resampling based...
Clustering is an effective approach for managing nodes in wireless sensor networks (WSN). In this paper, we consider selfish avoidance clustering in WSN. A differential game model is proposed to stimulate forwarding. Based on the payoff value of the differential game, called forwarding contribution (FC) in this paper, a centralized clustering algorithm is proposed. Mathematical proof shows that our...
K-anonymity is a model to protect public released microdata from individual identification. It requires that each record is identical to at least k-1 other records in the anonymized dataset with respect to a set of privacy-related attributes. Although it is easy to anonymize the original dataset to satisfy the requirement of k-anonymity, it is important to ensure that the anonymized dataset should...
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