Application of uninorms to market basket analysis

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The ability for grocery retailers to have a single view of customers across all their grocery purchases remains elusive and has become increasingly important in recent years (especially in the UK) where competition has intensified, shopping habits and demographics have changed and price sensitivity has increased following the 2008 recession. Numerous studies have been conducted on understanding independent items that are frequently bought together (association rule mining/ frequent itemsets) with several measures proposed to aggregate item support and rule confidence with varying levels of accuracy as these measures are highly context dependent. Uninorms were used as an alternative measure to aggregate support and confidence in analysing market basket data using the UK grocery retail sector as a case study. Experiments were conducted on consumer panel data with the aim of comparing the uninorm against three other popular measures (Jaccard, Cosine and Conviction). It was found that the uninorm outperformed other models on its adherence to the fundamental monotonicity property of support in market basket analysis. Future work will include the extension of this analysis to provide a generalised model for market basket analysis

Enhancing the Prediction of Missing Targeted Items from the Transactions of Frequent, Known Users

The ability for individual grocery retailers to have a single view of its customers across all of their grocery purchases remains elusive, and is considered the “holy grail” of grocery retailing. This has become increasingly important in recent years, especially in the UK, where competition has intensified, shopping habits and demographics have changed, and price sensitivity has increased. Whilst numerous studies have been conducted on understanding independent items that are frequently bought together, there has been little research conducted on using this knowledge of frequent itemsets to support decision making for targeted promotions. Indeed, having an effective targeted promotions approach may be seen as an outcome of the “holy grail”, as it will allow retailers to promote the right item, to the right customer, using the right incentives to drive up revenue, profitability, and customer share, whilst minimising costs. Given this, the key and original contribution of this study is the development of the market target (mt) model, the clustering approach, and the computer-based algorithm to enhance targeted promotions. Tests conducted on large scale consumer panel data, with over 32000 customers and 51 million individual scanned items per year, show that the mt model and the clustering approach successfully identifies both the best items, and customers to target. Further, the algorithm segregates customers into differing categories of loyalty, in this case it is four, to enable retailers to offer customised incentives schemes to each group, thereby enhancing customer engagement, whilst preventing unnecessary revenue erosion. The proposed model is compared with both a recently published approach, and the cross-sectional shopping patterns of the customers on the consumer scanner panel. Tests show that the proposed approach outperforms the other approach in that it significantly reduces the probability of having “false negatives” and “false positives” in the target customer set. Tests also show that the customer segmentation approach is effective, in that customers who are classed as highly loyal to a grocery retailer, are indeed loyal, whilst those that are classified as “switchers” do indeed have low levels of loyalty to the selected grocery retailer. Applying the mt model to other fields has not only been novel but yielded success. School attendance is improved with the aid of the mt model being applied to attendance data. In this regard, an action research study, involving the proposed mt model and approach, conducted at a local UK primary school, has resulted in the school now meeting the required attendance targets set by the government, and it has halved its persistent absenteeism for the first time in four years. In medicine, the mt model is seen as a useful tool that could rapidly uncover associations that may lead to new research hypotheses, whilst in crime prevention, the mt value may be used as an effective, tangible, efficiency metric that will lead to enhanced crime prevention outcomes, and support stronger community engagement. Future work includes the development of a software program for improving school attendance that will be offered to all schools, while further progress will be made on demonstrating the effectiveness of the mt value as a tangible crime prevention metric

Using Data Mining in Educational Administration - A Case Study on Improving School Attendance

open access articlePupil absenteeism remains a significant problem for schools across the globe with its negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96\%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, show that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes

Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools

Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

The authors would like to thank the leadership and staff of Willen Primary School for permitting us to use their data and for their efforts in supporting this study, in particular, Ms Emma Warner (attendance officer), Ms Carrie Matthews (headteacher), and Ms Sarah Orr (deputy headteacher).Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter change

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions