Mininng High Utility Itemsets (HUI) with negative items is one of the emerging itemsets mining tasks. In order to mine the result set of HUI with negative items meeting the user needs, a Top- k High utility itemsets with Negative items (THN) mining algorithm was proposed. In order to improve the temporal and spatial performance of the THN algorithm, a strategy to automatically increase the minimum utility threshold was proposed, and the pattern growth method was used for depth-first search; the search space was pruned by using the redefined subtree utility and the redefined local utility; the transaction merging technology and dataset projection technology were employed to solve the problem of scanning the database for multiple times; in order to increase the utility counting speed, the utility array counting technology was used to calculate the utility of the itemset. Experimental results show that the memory usage of THN algorithm is about 1/60 of that of the HUINIV (High Utility Itemsets with Negative Item Values)-Mine algorithm, and is about 1/2 of that of the FHN (Faster High utility itemset miner with Negative unit profits) algorithm; the THN algorithm takes 1/10 runtime of that of the FHN algorithm; and the THN algorithm achieves better performance on dense datasets.

Focusing on medical image secure retrieval in cloud server, an encrypted medical image retrieval algorithm based on Discrete Wavelet Transform (DWT) and perceptual hash was proposed. Firstly, the image was encrypted in frequency domain based on the characteristics of Henon mapping. Secondly, the encrypted medical image was decomposed by wavelet to obtain the sub-image close to the original image. According to the characteristics of Discrete Cosine Transform (DCT), the perceptual hash sequence of the image was obtained by comparing the relationship between the coefficients of DCT and the mean of the coefficients. Finally, the encrypted medical image retrieval was achieved by comparing the normalized correlation coefficients between the perceived hash sequences. Compared with the hash algorithm based on Non-negative Matrix Factorization (NMF), the proposed algorithm improves the retrieval accuracy by nearly 40% under Gaussian noise, which is not changed obviously under the JPEG compression attack, median filter attack, scaling attack and ripple distortion attack. Experimental results show that the proposed algorithm has strong robustness against geometric attack and conventional attack, as well as reduce the time complexity of image encryption.