A Weighted Utility Framework for Mining Association Rules

Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by assuming that all items have the same significance and frequency of occurrence in a record i.e. their weight and utility is the same (weight=1 and utility=1) which is not always the case. However, items are actually different in many aspects in a number of real applications such as retail marketing, nutritional pattern mining etc. These differences between items may have a strong impact on decision making in many application unlike the use of standard ARM. Our framework, Weighted Utility ARM (WUARM), considers the varied significance and different frequency values of individual items as their weights and utilities. Thus, weighted utility mining focuses on identifying the itemsets with weighted utilities higher than the user specified weighted utility threshold. We conduct experiments on synthetic and real data sets using standard ARM, weighted ARM and Weighted Utility ARM (WUARM) and present analysis of the results. 1

Finding associations in composite data sets

In this paper, a composite fuzzy association rule mining mechanism CFARM, directed at identifying patterns in datasets comprised of composite attributes, is described. Composite attributes are defined as attributes that can take simultaneously two or more values that subscribe to a common schema. The objective is to generate fuzzy association rules using "properties" associated with these composite attributes. The exemplar application is the analysis of the nutrients contained in items found in grocery data sets. The paper commences with a review of the back ground and related work, and a formal definition of the CFARM concepts. The CFARM algorithm is then fully described and evaluated using both real and synthetic data sets

Towards Healthy Association Rule Mining (HARM): A Fuzzy Quantitative Approach

Abstract. Association Rule Mining (ARM) is a popular data mining technique that has been used to determine customer buying patterns. Although improving performance and efficiency of various ARM algorithms is important, determining Healthy Buying Patterns (HBP) from customer transactions and association rules is also important. This paper proposes a framework for mining fuzzy attributes to generate HBP and a method for analysing healthy buying patterns using ARM. Edible attributes are filtered from transactional input data by projections and are then converted to Required Daily Allowance (RDA) numeric values. Depending on a user query, primitive or hierarchical analysis of nutritional information is performed either from normal generated association rules or from a converted transactional database. Query and attribute representation can assume hierarchical or fuzzy values respectively. Our approach uses a general architecture for Healthy Association Rule Mining (HARM) and prototype support tool that implements the architecture. The paper concludes with experimental results and discussion on evaluating the proposed framework