Abstract
BackgroundEmerging pattern mining is a data mining task that extracts rules describing discriminative relationships amongst variables. These rules should be understandable for the experts. Comprehensibility of a rule is traditionally determined by several objectives, which can be calculated by different measures. In this way, multi-objective evolutionary algorithms are suitable for this task. Currently, the growing amount of data makes traditional data mining tasks unable to process them in a reasonable time. These huge amounts of data make even more interesting the extraction of rules that can easily describe the underlying phenomena of this big data. So far there is only one algorithm for emerging pattern mining developed based on multi-objective evolutionary algorithms for big data, the BD-EFEP algorithm. The influence of the selection of different quality measures as objectives in the search process is analysed in this paper.ResultsThe results show that the use of the combination based on Jaccard index and false positive rate is the one with the best trade-off for descriptive induction of emerging patterns.ConclusionsIt is recommended the use of this combination of quality measure as optimisation objectives in future multi-objective evolutionary algorithm developments for emerging pattern mining focused in big data.
Highlights
The amount of information generated everyday has suffered an exponential growth since the last decades
This paper presents a study about the suitability of the combination of quality measures in order to be used as objectives for a multi-objective approach for the extraction of emerging patterns (EPs) in Big Data environments
The algorithm used is the BD-EFEP algorithm, a multi-objective evolutionary algorithm focused in Big Data
Summary
The amount of information generated everyday has suffered an exponential growth since the last decades. Big data is related to the amount of data but is related to the variety of sources where these data come from and the arrival velocity into the system This amount of data should be analysed for the extraction of valuable knowledge that can ease the decision making processes. Emerging pattern mining is a data mining task that extracts rules describing discriminative relationships amongst variables. These rules should be understandable for the experts. The growing amount of data makes traditional data mining tasks unable to process them in a reasonable time These huge amounts of data make even more interesting the extraction of rules that can describe the underlying phenomena of this big data.
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