Association Rule

This chapter contains an overview of intelligent techniques that can be applied in different stages of e-learning systems to achieve personalization. It describes examples of their application to various e-learning platforms to create profiles of learners and to define learning path. The typical approach to obtain learner's profile is the usage one of the clustering methods, such as: the simple kmeans, Self Organizing Map, hierarchical clustering or fuzzy clustering. Classification methods like: C4.5 or C.5, k-Nearest Neighbor and Naive Bayes are also useful, but they need to define classes and training patterns by an expert. In contrary, clustering is unsupervised learning method and the categories are discovered by the method itself. The recommending system is responsible for proposing individual learning path for each learner. The most popular approach is an application of the Aprori method which searches for association rules. However, it seems that it is rather inefficient method when the number of data to process is huge. Other methods and models that can be useful for knowledge representation are also discussed. Recommending systems are mainly built as a knowledge based. Most of them are implemented as rule based systems. An interesting approach implementing cased based reasoning paradigm to recommend learning path is described as well. The end of the chapter contains a critical discussion of existing solutions and suggests possible research in this field.

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Despite the numerous research endeavors aimed at investigating tourists' preferences and motivations, it remains very difficult for practitioners to utilize the results of traditional association rule mining methods in tourism management. This research presents a new approach that extends the capability of the association rules technique to contrast targeted association rules with the aim of capturing the changes and trends in outbound tourism. Using datasets collected from five large-scale domestic tourism surveys of Hong Kong residents on outbound pleasure travel, both positive and negative contrasts are identified, thus enabling practitioners and policymakers to make appropriate decisions and develop more appropriate tourism products.

It has been claimed that the discovery of association rules is well-suited for applications of market basket analysis to reveal regularities in the purchase behaviour of customers. Moreover, recent work indicates that the discovery of interesting rules can in fact only be addressed within a microeconomic framework. This study integrates the discovery of frequent itemsets with a (microeconomic) model for product selection (PROFSET). The model enables the integration of both quantitative and qualitative (domain knowledge) criteria. Sales transaction data from a fullyautomated convenience store is used to demonstrate the effectiveness of the model against a heuristic for product selection based on product-specific profitability. We show that with the use of frequent itemsets we are able to identify the cross-sales potential of product items and use this information for better product selection. Furthermore, we demonstrate that the impact of product assortment decisions on overall assortment profitability can easily be evaluated by means of sensitivity analysis.

This paper introduces architecture for recommender system in domain of interlibrary services. The system works based on rules that was extracted from historical usage logs by the aid of data mining techniques. The paper also describes experimental results of pilot implementation of system for Ghadir project (resource sharing program in fran). The pilot design and implementation phases were data gathering, pre processing and warehousing, data mining and association rules extraction that all were described in the paper. The logs of 10 years usage of Ghadir project were processed and stored in data warehouse. Then eLOPE algorithm was applied on data to cluster users. After that decision tree technique was used for rule extraction.

ABSTRACT: Association rules are one of the most researched areas of data mining and have recently received much attention from the database community. They have proven to be quite useful in the marketing and retail communities as well as other more diverse ...