Mining minimal non-redundant association rules using frequent closed itemsets

The problem of the relevance and the usefulness of extracted association rules is of primary importance because, in the majority of cases, real-life databases lead to several thousands association rules with high confidence and among which are many redundancies. Using the closure of the Galois connection, we define two new bases for association rules which union is a generating set for all valid association rules with support and confidence. These bases are characterized using frequent closed itemsets and their generators; they consist of the non-redundant exact and approximate association rules having minimal antecedents and maximal consequences, i.e. the most relevant association rules. Algorithms for extracting these bases are presented and results of experiments carried out on real-life databases show that the proposed bases are useful, and that their generation is not time consuming.

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.

Chain Split and Computations in Practical Rule Mining

A novel association rule mining algorithm is composed, using the unit cube chain decomposition structures introduced in [HAN, 1966; TON, 1976]. [HAN, 1966] established the chain split theory. [TON, 1976] invented an excellent chain computation framework which brings chain split into the practical domain. We integrate these technologies around the rule mining procedures. Effectiveness is related to the intention of low complexity of rules mined. Complexity of the procedure composed is complementary to the known Apriori algorithm which is defacto standard in rule mining area.

Mining dynamic databases for frequent closed itemsets

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Mining dynamic databases for frequent closed itemsets

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Association study of an epidermal growth factor gene functional polymorphism with the risk and prognosis of gliomas in Brazil

Epidermal growth factor (EGF) plays an important role in cancer. A functional single nucleotide polymorphism (SNP) in the 5`-untranslated region of the EGF gene (+61 A>G) may influence its expression and contribute to cancer predisposition and aggressiveness. Aiming to investigate the role of EGF +61 A>G in the susceptibility to glioma and its prognosis, we performed a case-control study with 165 patients and 200 healthy controls from Brazil. Comparisons of genotype distributions and allele frequencies did not reveal any significant differences between the groups. The mean overall survival was 9.2 months for A/A, 8.2 months for A/G, and 7.7 months for GIG. When survival curves were plotted we found that the +61G allele is associated with poor overall survival (p=0.023) but not with disease-free survival (p=0.527). Our data suggest that, although there is no association between the EGF +61 A>G genotype and glioma susceptibility, this SNP is associated with shorter overall survival of glioma patients in the Brazilian population. Nevertheless, future studies utilizing a larger series are essential for a definitive conclusion. (Int J Biol Markers 2009; 24: 277-81)

Association of genetic variants in complement factor H and factor H-related genes with systemic lupus erythematosus susceptibility

Systemic lupus erythematosus (SLE), a complex polygenic autoimmune disease, is associated with increased complement activation. Variants of genes encoding complement regulator factor H (CFH) and five CFH-related proteins (CFHR1-CFHR5) within the chromosome 1q32 locus linked to SLE, have been associated with multiple human diseases and may contribute to dysregulated complement activation predisposing to SLE. We assessed 60 SNPs covering the CFH-CFHRs region for association with SLE in 15,864 case-control subjects derived from four ethnic groups. Significant allelic associations with SLE were detected in European Americans (EA) and African Americans (AA), which could be attributed to an intronic CFH SNP (rs6677604, in intron 11, Pmeta = 6.6×10-8, OR = 1.18) and an intergenic SNP between CFHR1 and CFHR4 (rs16840639, Pmeta = 2.9×10-7, OR = 1.17) rather than to previously identified disease-associated CFH exonic SNPs, including I62V, Y402H, A474A, and D936E. In addition, allelic association of rs6677604 with SLE was subsequently confirmed in Asians (AS). Haplotype analysis revealed that the underlying causal variant, tagged by rs6677604 and rs16840639, was localized to a ~146 kb block extending from intron 9 of CFH to downstream of CFHR1. Within this block, the deletion of CFHR3 and CFHR1 (CFHR3-1Δ), a likely causal variant measured using multiplex ligation-dependent probe amplification, was tagged by rs6677604 in EA and AS and rs16840639 in AA, respectively. Deduced from genotypic associations of tag SNPs in EA, AA, and AS, homozygous deletion of CFHR3-1Δ (Pmeta = 3.2×10-7, OR = 1.47) conferred a higher risk of SLE than heterozygous deletion (Pmeta = 3.5×10-4, OR = 1.14). These results suggested that the CFHR3-1Δ deletion within the SLE-associated block, but not the previously described exonic SNPs of CFH, might contribute to the development of SLE in EA, AA, and AS, providing new insights into the role of complement regulators in the pathogenesis of SLE.

Association of an insulin-like growth factor 1 gene microsatellite with phenotypic variation and estimated breeding values of growth traits in Canchim cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Association of the Cell Cycle Transcription Factor Mbp1 with the Skn7 Response Regulator in Budding Yeast

We previously isolated the SKN7 gene in a screen designed to isolate new components of the G1-S cell cycle transcription machinery in budding yeast. We have now found that Skn7 associates with Mbp1, the DNA-binding component of the G1-S transcription factor DSC1/MBF. SKN7 and MBP1 show several genetic interactions. Skn7 overexpression is lethal and is suppressed by a mutation in MBP1. Similarly, high overexpression of Mbp1 is lethal and can be suppressed by skn7 mutations. SKN7 is also required for MBP1 function in a mutant compromised for G1-specific transcription. Gel-retardation assays indicate that Skn7 is not an integral part of MBF. However, a physical interaction between Skn7 and Mbp1 was detected using two-hybrid assays and GST pulldowns. Thus, Skn7 and Mbp1 seem to form a transcription factor independent of MBF. Genetic data suggest that this new transcription factor could be involved in the bud-emergence process.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Mining association rules in folksonomies

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.

Efficient mining of association rules based on formal concept analysis

Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, TITANIC, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.