An Evolutionary Model to Mine High Expected Utility Patterns From Uncertain Databases
Abstract : 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 in-teresting 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, hyper volume and number of the discovered patterns compared to the generic approaches.
Data mining consists of deriving implicit, potentially meaningful and useful knowledge from databases such as information about the most profitable items.
High-utility item set mining (HUIM) has thus emerged as an important research topic in data mining.
But most HUIM algorithms can only handle precise data, although big data collected in real-life applications using experimental measurements or noisy sensors is often uncertain.
In this paper, an efficient algorithm, named Mining Uncertain High-Utility Item sets (MUHUI), is proposed to efficiently discover potential high-utility item sets (PHUIs) in uncertain data.
• The generic Evolutionary Computation (EC) [15] is a meta-heuristic approach, which is used to solve the NP-hard and optimization problems efficiently based on the single-objective fitness function in evolutionary progress.
• MOEA/D is a generic framework that integrates the multi-objective evolutionary problem into small multi-objective opti-mization subproblems.
• It is not a trivial task to set the appropriate thresholdfor pattern evaluation since it can easily cause the “rare-item”and “combinational explosion” problems.
? This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques.
? The set of transactions is first clustered, such that highly correlated transactions are grouped together.
? Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster.
? We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy.
? Experiments are then conducted to show the effectiveness and efficiency of the designed MOEA-HEUPM model in terms of convergence, hyper volume and number of the discovered patterns compared to the generic approaches.
? The HV and Cov are then evaluated to verify the efficiency of two binary and value encoding schemas. The results for the two variants of encoding schemas are then showed in Figs. 2 and 3, respectively.
? high-lighted the performance issue with the current incremental high utility mining algorithm that has a high false discovery rate and generates many patterns.
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