Market basket analysis (also known as association-rule mining) is a useful method of discovering customer purchasing patterns by extracting associations or co-occurrences from stores’ transactional databases. Because the information obtained from the analysis can be used in forming marketing, sales, service, and operation strategies, it has drawn increased research interest. The existing method...

Subgroup discovery consists in finding subsets of individuals from a given population which have distinctive collective properties with regard to one or more properties of interest. The interest of a subgroup can be objectively assessed using appropriate statistics, but it can also be evaluated by a data analyst or domain expert. In this paper we propose an approach to subgroup discovery via di...

In the last years, new clustering approaches utilizing the notion of multiple clusterings have gained attention. Two general directions — each with its individual benefits — are identifiable: (i) extraction of multiple alternative clustering solutions from one dataset and (ii) combination of multiple clusterings of a dataset into one robust consensussolution. In this paper, we propose a novel h...

Frequent itemsets and association rules are generally accepted concepts in analyzing item-based databases. The Apriori-framework was developed for analyzing categorical data. However, many data include numerical values. Therefore, most existing techniques transform numerical values to categorical values. The transformation is done such that the rules are optimal with respect to support or confi...

As a side effect of unprecedented amount of digitization of data, classical retrieval tools found themselves unable to go further beyond the tip of the Iceberg. Data Mining in conjunction with the Formal Concept Analysis, is a clear promise to furnish adequate tools to do so and specially to be able to derive concise generic and easy understandable bases of ”hidden” knowledge, that can be relia...

Association rule mining is an important data analysis tool that can be applied with success to a variety of domains. However, most association rule mining algorithms seek to discover statistically significant patterns (i.e. those with considerable support). We argue that, in law-enforcement, intelligence and counterterrorism work, sometimes it is necessary to look for patterns which do not have...

  Background :Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) is an important negative regulator of T-cell responses. The -1722TC polymorphism of the CTLA-4 gene may be associated with systemic lupus erythematosus (SLE) risk, but related results from previous studies have been inconsistent. We carried out a meta-analysis to assess this association more precisely.   Methods : A systematic s...

The decreasing cost of performing genome-wide association studies has made genomics widely accessible. However, there is a paucity of guidance for best practice in conducting such analyses. For the results of a study to be valid and replicable, multiple biases must be addressed in the course of data preparation and analysis. In addition, standardizing methods across small, independent studies w...

A method to analyse links between binary attributes in a large sparse data set is proposed. Initially the variables are clustered to obtain homogeneous clusters of attributes. Association rules are then mined in each cluster. A graphical comparison of some rule relevancy indexes is presented. It is used to extract best rules depending on the application concerned. The proposed methodology is il...

Association rules have exhibited an excellent ability to identify interesting association relationships among a set of binary variables describing huge amount of transactions. Although the rules can be relatively easily generalized to other variable types, the generalization can result in a computationally expensive algorithm generating a prohibitive number of redundant rules of little signific...