高效用占有模式挖掘论文在国际期刊IEEE TCYB在线发表

      最近，哈工大深圳的甘文生博士关于效用挖掘的论文"HUOPM: High Utility Occupancy Pattern Mining", 先后历时24个月的peer-review, 在人工智能等领域的权威期刊IEEE Transactions on Cybernetics (SCI, IF:10.387, JCR Q1, 中科院一区, CCF B) 在线发表, DOI: 10.1109/TCYB.2019.2896267。哈尔滨工业大学(深圳)为论文的第一作者单位, 该项研究得到了国家自然科学青年基金、深圳市孔雀计划专项和中国国家留学基金的资助。本文的完成人包括哈工大深圳的甘文生、西挪威应用科技大学的林浚玮教授 (原哈工大深圳的副教授, 已于2018年6月离职)、哈工大深圳的Philippe Fournier-Viger教授，台湾东华大学的赵涵捷教授、美国伊利诺伊大学芝加哥分校的Philip S. Yu教授。

论文链接：https://ieeexplore.ieee.org/abstract/document/8645787

       该论文提出一个基于效用占有（Utility Occupancy）衡量准则的高效用占有模式挖掘算法。该HUOPM算法首次提出两个高度压缩的数据结构：效用占有列表和频率效用表，用于存储事务数据中各个数据项的频度和效用信息；同时首次提出了剩余效用占有的概念，用于计算出效用占有度的上界值，从而减少实际的搜索空间。大量的实验结果表明 HUOPM 算法可以从事务型数据中有效地挖掘出有价值的高效用占有模式，而且能保证挖掘结果完整不遗漏，挖掘性能表现好。该算法成功解决了挖掘高效用占有模式的新研究问题，相关概念与技术有望扩展到处理其他类型的数据，有助于进一步扩大效用挖掘的内涵与外延。 

IEEE Transactions on Cybernetics (SCI, IF:10.387,  JCR Q1, 中科院一区, CCF B), IEEE TCYB是计算机科学的人工智能领域具有高影响力的国际学术刊物之一，在2018年该领域120余种JCR期刊中排名前列，影响因子为10.387，中科院一区，主要发表和报道计算智能、人工智能、数据科学、神经网络、遗传算法、机器学习、模糊系统、认知系统等领域的最新研究进展和技术。

论文题目: HUOPM: High-Utility Occupancy Pattern Mining

文章链接：https://ieeexplore.ieee.org/abstract/document/8645787

Authors:  Wensheng Gan, Jerry Chun-Wei Lin*, Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu

Abstract:

        Mining useful patterns from varied types of databases is an important research topic, which has many reallife applications. Most studies have considered the frequency as sole interestingness measure to identify high-quality patterns. However, each object is different in nature. The relative importance of objects is not equal, in terms of criteria, such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, that is, they often represent small sets of items in transactions containing many items and, thus, may not be truly representative of these transactions. To extract high-quality patterns in real-life applications, this paper extends the occupancy measure to also assess the utility of patterns in transaction databases. We propose an efficient algorithm named high-utility occupancy pattern mining (HUOPM). It considers user preferences in terms of frequency, utility, and occupancy. A novel frequency-utility tree and two compact data structures, called the utility-occupancy list and frequency-utility table, are designed to provide global and partial downward closure properties for pruning the search space. The proposed method can efficiently discover the complete set of high-quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of the roposed algorithm. Results show that the derived patterns are intelligible, reasonable, and acceptable, and that HUOPM with its pruning strategies outperforms the state-of-the-art algorithm, in terms of runtime and search space, respectively.