An Evolutionary Model to Mine High Expected Utility Patterns From Uncertain Databases

In recent decades, mobile or the Internet of Thing (IoT) devices are dramatically increasing in many domains and applications. Thus, a massive amount of data is generated and produced. Those collected data contain a large amount of interesting information (i.e., interestingness, weight, frequency, or uncertainty), and most of the existing and generic algorithms in pattern mining only consider the single object and precise data to discover the required information. Meanwhile, since the collected information is huge, and it is necessary to discover meaningful and up-to-date information in a limit and particular time. In this paper, we consider both utility and uncertainty as the majority objects to efficiently mine the interesting high expected utility patterns (HEUPs) in a limit time based on the multi-objective evolutionary framework. The benefits of the designed model (called MOEA-HEUPM) can discover the valuable HEUPs without pre-defined threshold values (i.e., minimum utility and minimum uncertainty) in the uncertain environment. Two encoding methodologies are also considered in the developed MOEA-HEUPM to show its effectiveness. Based on the developed MOEA-HEUPM model, the set of non-dominated HEUPs can be discovered in a limit time for decision-making. Experiments are then conducted to show the effectiveness and efficiency of the designed MOEA-HEUPM model in terms of convergence, hypervolume and number of the discovered patterns compared to the generic approaches.

[1]  Unil Yun,et al.  Analysis of tree-based uncertain frequent pattern mining techniques without pattern losses , 2016, The Journal of Supercomputing.

[2]  Lu Yang,et al.  A binary PSO approach to mine high-utility itemsets , 2017, Soft Comput..

[3]  Enhong Chen,et al.  Revisiting bound estimation of pattern measures: A generic framework , 2016, Inf. Sci..

[4]  Unil Yun,et al.  A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives , 2017, Future Gener. Comput. Syst..

[5]  Ramakrishnan Srikant,et al.  The Quest Data Mining System , 1996, KDD.

[6]  Tzung-Pei Hong,et al.  Weighted frequent itemset mining over uncertain databases , 2015, Applied Intelligence.

[7]  Hans-Peter Kriegel,et al.  Probabilistic frequent itemset mining in uncertain databases , 2009, KDD.

[8]  Kandhasamy Premalatha,et al.  Discovery of High Utility Itemsets Using Genetic Algorithm with Ranked Mutation , 2014, Appl. Artif. Intell..

[9]  Jerry Chun-Wei Lin,et al.  Metaheuristics for Frequent and High-Utility Itemset Mining , 2019, Studies in Big Data.

[10]  Le Gruenwald,et al.  A survey of data mining and knowledge discovery software tools , 1999, SKDD.

[11]  Vincent S. Tseng,et al.  Mining High-Utility Itemsets with Both Positive and Negative Unit Profits from Uncertain Databases , 2017, PAKDD.

[12]  A. Choudhary,et al.  A fast high utility itemsets mining algorithm , 2005, UBDM '05.

[13]  Yansen Su,et al.  A multi-objective evolutionary approach for mining frequent and high utility itemsets , 2018, Appl. Soft Comput..

[14]  Ying-Ho Liu,et al.  Mining time-interval univariate uncertain sequential patterns , 2015, Data Knowl. Eng..

[15]  Tzung-Pei Hong,et al.  Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules , 2020, Sensors.

[16]  Lu Yang,et al.  Mining high-utility itemsets based on particle swarm optimization , 2016, Eng. Appl. Artif. Intell..

[17]  Justin Zhijun Zhan,et al.  An ACO-based approach to mine high-utility itemsets , 2017, Knowl. Based Syst..

[18]  Mengchi Liu,et al.  Mining high utility itemsets without candidate generation , 2012, CIKM.

[19]  Antonio Gomariz,et al.  The SPMF Open-Source Data Mining Library Version 2 , 2016, ECML/PKDD.

[20]  Van-Nam Huynh,et al.  Mining closed high utility itemsets in uncertain databases , 2016, SoICT.

[21]  Unil Yun,et al.  Efficient approach for incremental high utility pattern mining with indexed list structure , 2019, Future Gener. Comput. Syst..

[22]  Heungmo Ryang,et al.  Top-k high utility pattern mining with effective threshold raising strategies , 2015, Knowl. Based Syst..

[23]  Philippe Fournier-Viger,et al.  Uncertainty-Based Pattern Mining for Maximizing Profit of Manufacturing Plants With List Structure , 2020, IEEE Transactions on Industrial Electronics.

[24]  Ruifeng Shi,et al.  Multi-Objective Optimization of Electric Vehicle Fast Charging Stations with SPEA-II , 2015 .

[25]  A. Kumar.  ENCODING SCHEMES IN GENETIC ALGORITHM , 2013 .

[26]  Vincent S. Tseng,et al.  FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning , 2014, ISMIS.

[27]  Ye Tian,et al.  An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[28]  Kalyanmoy Deb,et al.  A survey of evolutionary algorithms using metameric representations , 2019, Genetic Programming and Evolvable Machines.

[29]  M. Hadi Amini,et al.  Evolutionary Computation, Optimization and Learning Algorithms for Data Science , 2019, Advances in Intelligent Systems and Computing.

[30]  Vincent S. Tseng,et al.  EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining , 2015, MICAI.

[31]  Philippe Fournier-Viger,et al.  A survey of itemset mining , 2017, WIREs Data Mining Knowl. Discov..

[32]  Philip S. Yu,et al.  A Survey of Parallel Sequential Pattern Mining , 2018, ACM Trans. Knowl. Discov. Data.

[33]  Ujjwal Maulik,et al.  A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I , 2014, IEEE Transactions on Evolutionary Computation.

[34]  Christian Gagné,et al.  Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space , 2012, GECCO '12.

[35]  Philip S. Yu,et al.  A Survey of Utility-Oriented Pattern Mining , 2018, IEEE Transactions on Knowledge and Data Engineering.

[36]  R. Agarwal.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[37]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[38]  Ujjwal Maulik,et al.  Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II , 2014, IEEE Transactions on Evolutionary Computation.

[39]  Tzung-Pei Hong,et al.  Efficient algorithms for mining high-utility itemsets in uncertain databases , 2016, Knowl. Based Syst..

[40]  Tzung-Pei Hong,et al.  Efficiently mining uncertain high-utility itemsets , 2017, Soft Comput..

[41]  Carson Kai-Sang Leung,et al.  Mining interesting patterns from uncertain databases , 2016, Inf. Sci..

[42]  Osmar R. Zaïane,et al.  UAPRIORI: AN ALGORITHM FOR FINDING SEQUENTIAL PATTERNS IN PROBABILISTIC DATA , 2012 .

[43]  Qingfu Zhang,et al.  Adaptive Replacement Strategies for MOEA/D , 2016, IEEE Transactions on Cybernetics.

[44]  Inés Couso,et al.  Sequential pattern mining applied to aeroengine condition monitoring with uncertain health data , 2015, Eng. Appl. Artif. Intell..

[45]  Heungmo Ryang,et al.  An uncertainty-based approach: Frequent itemset mining from uncertain data with different item importance , 2015, Knowl. Based Syst..

[46]  Unil Yun,et al.  Efficient High Utility Pattern Mining for Establishing Manufacturing Plans With Sliding Window Control , 2017, IEEE Transactions on Industrial Electronics.