Abstract-Utility mining emerged recently to address the limitation of frequent itemset mining by introducing interestingness measures that reflect both the statistical significance and the user's expectation. Among utility mining problems, utility mining with the itemset share framework is a hard one as no anti-monotone property holds with the interestingness measure. The state-of-the-art works on this problem all employ a two-phase, candidate generation approach, which suffers from the scalability issue due to the huge number of candidates. This paper proposes a high utility itemset growth approach that works in a single phase without generating candidates. Our basic approach is to enumerate itemsets by prefix extensions, to prune search space by utility upper bounding, and to maintain original utility information in the mining process by a novel data structure. Such a data structure enables us to compute a tight bound for powerful pruning and to directly identify high utility itemsets in an efficient and scalable way. We further enhance the efficiency significantly by introducing recursive irrelevant item filtering with sparse data, and a lookahead strategy with dense data. Extensive experiments on sparse and dense, synthetic and real data suggest that our algorithm outperforms the state-of-the-art algorithms over one order of magnitude.
Medical systems allow patients to receive care at different hospitals. However, this entails considerable inconvenience through the need to transport patients and their medical records between hospitals. The development of Telecare Medicine Information Systems (TMIS) makes it easier for patients to seek medical treatment and to store and access medical records. However, medical data stored in TMIS is not encrypted, leaving patients' private data vulnerable to external leaks. In 2014, scholars proposed a new cloud-based medical information model and authentication scheme which would not only allow patients to remotely access medical services but also protects patient privacy. However, this scheme still fails to provide patient anonymity and message authentication. Furthermore, this scheme only stores patient medical data, without allowing patients to directly access medical advice. Therefore, we propose a new authentication scheme, which provides anonymity, unlinkability, and message authentication, and allows patients to directly and remotely consult with doctors. In addition, our proposed scheme is more efficient in terms of computation cost. The proposed system was implemented in Android system to demonstrate its workability.
Separation membranes with high performance can potentially be made by incorporating zeolites (or other nanoporous molecular sieves) in polymeric materials. However, the fabrication of technologically viable membranes has been hampered by poor adhesion between the inorganic crystals and the polymer and by inadequate dispersion of the inorganic particles. We report a facile, high-yield, and inexpensive solvothermal deposition process to prepare roughened inorganic Mg(OH)(2) nanostructures on zeolite (MFI) crystal surfaces in a controlled manner. The functionalized zeolite crystals result in high-quality "mixed matrix" membranes, wherein the zeolite crystals are well-adhered to the polymeric matrix. Substantially enhanced CO(2) and CH(4) gas permeation characteristics were observed in mixed matrix membranes containing up to 35 wt % of solvothermally modified MFI crystals. Gas permeation measurements on membranes containing nonporous uncalcined MFI revealed that the performance enhancements are indeed due to significantly enhanced MFI-polymer adhesion and distribution of MFI crystals.
Abstract. In this paper, we propose an efficient algorithm, called TD-FPGrowth (the shorthand for Top-Down FP-Growth), to mine frequent patterns. TD-FP-Growth searches the FP-tree in the top-down order, as opposed to the bottom-up order of previously proposed FP-Growth. The advantage of the topdown search is not generating conditional pattern bases and sub-FP-trees, thus, saving substantial amount of time and space. We extend TD-FP-Growth to mine association rules by applying two new pruning strategies: one is to push multiple minimum supports and the other is to push the minimum confidence. Experiments show that these algorithms and strategies are highly effective in reducing the search space.
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