从不确定数据中发现高效用占用模式
Discovering High Utility-Occupancy Patterns from Uncertain Data
摘要：
众所周知，大数据中隐藏着大量有用的信息，导致了一种新的说法“数据就是金钱。“因此，个人挖掘关键信息以供许多现实世界的应用程序使用是很普遍的。在过去，研究已经考虑了频率。不幸的是，这样做忽略了其他方面，如效用、利益或风险。因此，它是明智的，发现高效用项目集（HUIs）在事务数据库中，同时利用不仅数量，但也预定义的效用。为了找到可以代表支持交易的模式，最近进行了一项研究，以挖掘高效用占用模式，其对整个交易的效用的贡献大于一定的值。此外，在实际应用中，模式可能不存在于交易中，但与存在概率有关。提出了一种新的不确定数据库中高效用占用模式挖掘算法（UHUOPM）。该算法发现的模式被称为潜在高效用占用模式（PHUOP）。该算法将用户偏好分为支持度、概率和效用占用三个因素。为了减少存储器成本和时间消耗，并修剪搜索空间在上述算法中，概率效用占用列表（PUO-列表）和概率频率效用表（PFU-表），这有助于提供向下闭合属性。此外，一个原始的树结构，称为支持计数树（SC-树），构造的搜索空间的算法。最后，大量的实验进行了评估所提出的UHUOPM算法在现实生活和合成数据集的性能，特别是在有效性和效率方面。
英文摘要：
It is widely known that there is a lot of useful information hidden in big data, leading to a new saying that "data is money." Thus, it is prevalent for individuals to mine crucial information for utilization in many real-world applications. In the past, studies have considered frequency. Unfortunately, doing so neglects other aspects, such as utility, interest, or risk. Thus, it is sensible to discover high-utility itemsets (HUIs) in transaction databases while utilizing not only the quantity but also the predefined utility. To find patterns that can represent the supporting transaction, a recent study was conducted to mine high utility-occupancy patterns whose contribution to the utility of the entire transaction is greater than a certain value. Moreover, in realistic applications, patterns may not exist in transactions but be connected to an existence probability. In this paper, a novel algorithm, called High-Utility-Occupancy Pattern Mining in Uncertain databases (UHUOPM), is proposed. The patterns found by the algorithm are called Potential High Utility Occupancy Patterns (PHUOPs). This algorithm divides user preferences into three factors, including support, probability, and utility occupancy. To reduce memory cost and time consumption and to prune the search space in the algorithm as mentioned above, probability-utility-occupancy list (PUO-list) and probability-frequency-utility table (PFU-table) are used, which assist in providing the downward closure property. Furthermore, an original tree structure, called support count tree (SC-tree), is constructed as the search space of the algorithm. Finally, substantial experiments were conducted to evaluate the performance of proposed UHUOPM algorithm on both real-life and synthetic datasets, particularly in terms of effectiveness and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
