The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications derive beneficial knowledge. For identifying evaluating the usefulness different kinds patterns, many techniques constraints have been proposed, such as support, confidence, sequence order, utility parameters (e.g., weight, price, profit, quantity, satisfac...

Utility mining has emerged as an important and interesting topic owing to its wide application considerable popularity. However, conventional utility methods have a bias toward items that longer on-shelf time they greater chance generate high utility. To eliminate the bias, problem of (OSUM) is introduced. In this article, we focus on task OSUM sequence data, where sequential database divided i...

Mining of High utility itemsets refers to discovering sets of data items that have high utilities. In recent years the high utility itemsets mining has extensive attentions due to the wide applications in various domains like biomedicine and commerce. Extraction of high utility itemsets from database is very problematic task. The formulated high utility itemset degrades the efficiency of the mi...

The paradigm shift from ‘data-centered pattern mining’ to ‘domain driven actionable knowledge discovery’ has increased the need for considering the business yield (utility) and demand or rate of recurrence of the items (frequency) while mining a retail business transaction database. Such a data mining process will help in mining different types of itemsets of varying business utility and demand...

Statistically significant pattern mining (SSPM), which evaluates each via a hypothesis test, is an essential and challenging data task for knowledge discovery. We introduce preference relation between patterns aim to discover the most preferred under constraint of statistical significance, has never been considered in existing SSPM problems. propose iterative multiple testing procedure that can...

Mining high utility itemsets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. In ...

The paradigm shift from 'data-centered pattern mining' to 'domain driven actionable knowledge discovery' has increased the need for considering the business yield (utility) and demand or rate of recurrence of the items (frequency) while mining a retail business transaction database. Such a data mining process will help in mining different types of itemsets of varying business utility and demand...

Utility mining is a new expansion of data mining expertise. Among utility mining difficulties, utility mining with the itemset share framework is a solid one as no anti-monotonicity property grasps with the interestingness amount. Preceding works on this problem all service a two-phase, candidate generation method with one exemption that is however incompetent and not mountable with large datab...