Analytics of high average-utility patterns in the industrial internet of things

Abstract Recently, revealing more valuable information except for quantity value a database is an essential research field. High utility itemset mining (HAUIM) was suggested to reveal useful patterns by average-utility measure pattern analytics and evaluations. HAUIM provides fair assessment than generic high ignores the influence of length itemsets. There are several high-performance algorithms proposed gain knowledge from disorganized database. However, most existing works do not concern uncertainty factor, which one characteristics data gathered IoT equipment. In this work, efficient algorithm handle databases in IoTs presented. Two upper-bound values estimated early diminish search space discovering meaningful that greatly solve limitations IoTs. Experimental results showed evaluations approach compared algorithms, acceptable state designed efficiently reveals average itemsets uncertain situation.