An optimized frequent pattern mining algorithm with multiple minimum supports
Hsiao-Wei Hu, Hao-Chen Chang, Wen-Shiu Lin · 2016
In the big data era, data mining techniques and applications are becoming increasingly important in various industries. Among numerous data mining techniques, frequent patterns are a crucial tool. The majority of existing studies on frequent pattern mining used single minimum support thresholds, which is unreasonable in the real world. Although there have been a lot of extensive research on support thresholds, particularly multiple minimum supports, their mining processes have all been extremely time-consuming. With advances in hardware and the maturing of cloud computing and distributed computing, the costs of storage media are lower, so we can get a look at the entirety of data. The internet offers numerous conveniences, which produced today's highly competitive industry. Obtaining crucial business information quickly from big data so as to make business decisions is a company's big challenge and key to success. We therefore proposed a high efficiency frequent pattern mining algorithm with multiple minimum supports based on the CFP-growth method, which is a frequent pattern mining approach aimed at multiple minimum support environments. It is hoped that this approach can provide a more efficient and advanced mining method to obtain hidden information that is crucial for corporations to make swift and accurate business decisions with. Our experiment results indicated that in the same execution environment, the overall efficiency of the proposed method is superior to the CFP-growth method.