Mining association rules may generate a large numbers of rules making the results hard to analyze manually. Pasquier et al. have discussed the generation of GuiguesDuquenne–Luxenburger basis (GD-L basis). Using a similar approach, we introduce a new rule of inference and define the notion of association rules cover as a minimal set of rules that are non-redundant with respect to this new rule o...

The potential of storing greenhouse gases in geological formations in the northeast portion of the Williston Basin is being investigated by researchers involved in the IEA Weyburn CO2 Monitoring and Storage Project. This project is linked to a CO2 miscible flood EOR program operated by EnCana Corporation in the Weyburn Midale Pool of southeastern Saskatchewan. A major component of the project i...

We consider the problem of mining association rules on a shared-nothing multiprocessor. We present three algorithms that explore a spectrum of trade-oos between computation, communication, memory usage, synchronization, and the use of problem-speciic information. The best algorithm exhibits near perfect scaleup behavior, yet requires only minimal overhead compared to the current best serial alg...

The discovery of association rules showing conditions of data co-occurrence has attracted the most attention in data mining. An example of an association rule is the rule “the customer who bought bread and butter also bought milk,” expressed by T(bread; butter)→T(milk). Let I ={x1,x2,...,xm} be a set of (data) items, called the domain; let D be a collection of records (transactions), where each...

When applying association mining to real datasets, a major obstacle is that often a huge number of rules are generated even with very reasonable support and confidence. Among these rules, many are trivial, redundant, semantically wrong, or already known by end-users. Association rule postprocessing aims to remove these undesired rules. Existing work mainly focuses on reducing redundant or findi...

Association rule mining is an important data mining problem. It is found to be useful for conventional relational data. However, previous work has mostly targeted on mining a single table. In real life, a database is typically made up of multiple tables and one important case is where some of the tables form a star schema. The tables typically correspond to entity sets and joining the tables in...

New application areas resulted in an increase of the diversity of the workloads that Data Base Management Systems have to confront. Resource management for mixed workloads is attained with the prioritization of their tasks, which during their execution may be forced to release some of their resources. In this paper, we consider workloads that consist of mixtures of OLTP transactions and associa...

In this paper we propose a method for extracting clusters in a population of customers, where the only information available is the list of products bought by the individual clients. We use association rules having high conndence to construct a hierarchical sequence of clusters. A speciic metric is introduced for measuring the quality of the resulting clusterings. Practical consequences are dis...

Weblogs are web sites where one or several authors publish their opinions about current events. Even in Spain, there are several thousands, and it is often difficult to find a weblog that meets one's interest. Recommendation services thus become, if not a need, at least a convenience. In this paper we propose automatic extraction of association rules from the results of a survey as a means to r...

The tutorial will start by reviewing the similarities and differences between statistics, machine learning and data mining. Then we will take a closer look at the knowledge discovery process as described by the CRISP-DM methodology. Here we will focus on various types of machine learning algorithms used for the modeling step and on the statistical approaches and methods used in these algorithms...