A Combinatorial Approach for High Utility Item Set Mining using FRUP and Direct Discovery Approach without Candidate Generation

The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques/constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e. g. , weight, price, profit, quantity, etc. ). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. In this research work we have undertook two different approach as proposed in [1] and [2]. One approach uses RUP/FRUP growth algorithm while the other method uses direct discovery algorithm which does not uses candidate generation. The FRUP/FRUP approach is more extensive in a sense that not only it is helpful in determining frequent itemset but it also helps in finding the utility of the item set in a more cohesive manner. We used Matlab programming environment to combine the two approaches. The experimental results show that RUP/FRUP when combined with direct discovery approach gives better results.