Mining High Utility Itemsets with Regular Occurrence

High utility itemset mining (HUIM) plays an important role in the data mining community and in a wide range of applications. For example, in retail business it is used for finding sets of sold products that give high profit, low cost, etc. These itemsets can help improve marketing strategies, make promotions/ advertisements, etc. However, since HUIM only considers utility values of items/itemsets, it may not be sufficient to observe product-buying behavior of customers such as information related to "regular purchases of sets of products having a high profit margin". To address this issue, the occurrence behavior of itemsets (in the term of regularity) simultaneously with their utility values was investigated. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) to find sets of co-occurrence items with high utility values and regular occurrence in a database was considered. An efficient single-pass algorithm, called MHUIRA, was introduced. A new modified utility-list structure, called NUL, was designed to efficiently maintain utility values and occurrence information and to increase the efficiency of computing the utility of itemsets. Experimental studies on real and synthetic datasets and complexity analyses are provided to show the efficiency of MHUIRA combined with NUL in terms of time and space usage for mining interesting itemsets based on regularity and utility constraints

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

Design of Machine Learning Framework for Products Placement Strategy in Grocery Store

The well-known and most used support-confidence framework for Association rule mining has some drawbacks when employ to generate strong rules, this weakness has led to its poor predictive performances. This framework predict customers buying behavior based on the assumption of the confidence value, which limits its competent at making good business decision. This work presents a better Association Rule Mining conceptualized framework for mining previous customers transactions dataset of grocery store for the optimal prediction of products placement on the shelves, physical shelf arrangement and identification of products that needs promotion. Sampled transaction records were used to demonstrate the proposed framework. The proposed framework leverage on the ability of lift metric at improving the predictive performance of Association Rule Mining. The Lift discloses how much better an association rule is at predicting products to be placed together on the shelve rather than assuming. The proposed conceptualized framework will assist retailers and grocery stores owners to easily unlock the latent knowledge or patterns in their large day to day stored transaction dataset to make important business decision that will make them competitive and maximized their profit margin