Reliable Association Rules Research Articles

Locomotor Training and Home Exercise Program to Improve Gait: Who May Benefit?

<h3>Research Objectives</h3> To determine the demographic, behavioral, and pathophysiological factors associated with the effectiveness of locomotor training and home exercise program in stroke survivors. <h3>Design</h3> This study was a secondary data analysis on the data collected from the Locomotor Experience Applied Post-Stroke (LEAPS) trial. The association rule, a data mining technique, was applied for the analysis. <h3>Setting</h3> General community. <h3>Participants</h3> We analyzed all 408 stroke survivors in the LEAPS trial. <h3>Interventions</h3> The participants were randomly assigned to receive three interventions: (1) Early Locomotor Training (ELT, started 2 months post-stroke); (2) Late Locomotor Training (LLT, started 6 months post-stroke); (3) Home Exercise Program (HEP, started 2 months post stroke). <h3>Main Outcome Measures</h3> Improvement of self-selected comfortable speed. <h3>Results</h3> Based on threshold we set for the Apriori algorithm, participants in the ELT who tended to perform better were white females, aged below 65 years, had private insurance, had a bachelor's degree or some college education, had no cardiovascular symptoms, and did not participate in physical activity on a weekly basis. Participants in the HEP who tended to perform better were white males, aged below 65 years, who had high school education GED, history of prior history of transient ischemic attack (TIA), and physical activity at least 3 times per week. No reliable association rules were identified in the LLT group. <h3>Conclusions</h3> The effectiveness of ELT and HEP may be predicted by patients' gender, race, age, education level, physical activity level, type of health insurance, cardiovascular symptoms, and/or history of TIA. The results may contribute to clinical decision making when selecting appropriate interventions such as locomotor training for stroke survivors. <h3>Author(s) Disclosures</h3> The authors declare that there is no conflict of interest.

A Grammar-Guided Genetic Programing Algorithm for Associative Classification in Big Data

The state-of-the-art in associative classification includes interesting approaches for building accurate and interpretable classifiers. These approaches generally work on four different phases (data discretization, pattern mining, rule mining, and classifier building), some of them being computational expensive. The aim of this work is to propose a novel evolutionary algorithm for efficiently building associative classifiers in Big Data. The proposed model works in only two phases (a grammar-guided genetic programming framework is performed in each phase): (1) mining reliable association rules; (2) building an accurate classifier by ranking and combining the previously mined rules. The proposal has been implemented on different architectures (multi-thread, Apache Spark and Apache Flink) to take advantage of the distributed computing. The experimental results have been obtained on 40 well-known datasets and analyzed through non-parametric tests. Results were compared to multiple approaches in the field and analyzed on three ways: quality of the predictions, level of interpretability, and efficiency. The proposed method obtained accurate and interpretable classifiers in an efficient way even on high-dimensional data, outperforming the state-of-the-art algorithms on three different levels: quality of the predictions, interpretability, and efficiency.

A new MapReduce associative classifier based on a new storage format for large-scale imbalanced data

The process of knowledge discovery from big and high dimensional datasets has become a popular research topic. The classification problem is a key task in bioinformatics, business intelligence, decision science, astronomy, physics, etc. Building associative classifiers has been a notable research interest in recent years because of their superior accuracy. In associative classifiers, using under-sampling or over-sampling methods for imbalanced big datasets reduces accuracy or increases running time, respectively. Hence, there is a significant need to create efficient associative classifiers for imbalanced big data problems. These classifiers should be able to handle challenges such as memory usage, running time and efficiently exploring the search space. To this end, efficient calculation of measures is a primary objective for associative classifiers. In this paper, we propose a new efficient associative classifier for big imbalanced datasets. The proposed method is based on Rare-PEARs (a multi-objective evolutionary algorithm that efficiently discovers rare and reliable association rules) and is able to evaluate rules in a distributed manner by using a new storing data format. This format simplifies measures calculation and is fully compatible with the MapReduce programming model. We have applied the proposed method (RPII) on a well-known big dataset (ECBDL’14) and have compared our results with seven other learning methods. The experimental results show that RPII outperform other methods in sensitivity and final score measures (the values of sensitivity and final score measures were approximately 0.74 and 0.54 respectively). The results demonstrate that the proposed method is a good candidate for large-scale classification problems; furthermore, it achieves reasonable execution time when the target platform is a typical computer clusters.

A comprehensive method for discovering the maximal frequent set

The association rule mining can be divided into two steps.The first step is to find out all frequent itemsets, whose occurrences are greater than or equal to the user-specified threshold.The second step is to generate reliable association rules based on all frequent itemsets found in the first step. Identifying all frequent itemsets in a large database dominates the overall performance in the association rule mining. In this paper, we propose an efficient method, INCREMENTAL PINCER, for discovering the maximal frequent itemsets. The INCREMENTAL PINCER method combines the advantages of both the DHP and the Pincer-Search algorithms. The combination leads to two advantages. First, the INCREMENTAL PINCER method, in general, can reduce the number of database scans. Second, the INCREMENTAL PINCER can filter the infrequent candidate itemsets and can use the filtered itemsets to find the maximal frequent itemsets. These two advantages can reduce the overall computing time of finding the maximal frequent itemsets. In addition, the INCREMENTAL PINCER method also provides an efficient mechanism to construct the maximal frequent candidate itemsets to reduce the search space. Keyterms: association rules, data mining, frequent itemsets, the INCREMENTAL PINCER method

Bioinformatics analysis of disordered proteins in prokaryotes

BackgroundA significant number of proteins have been shown to be intrinsically disordered, meaning that they lack a fixed 3 D structure or contain regions that do not posses a well defined 3 D structure. It has also been proven that a protein's disorder content is related to its function. We have performed an exhaustive analysis and comparison of the disorder content of proteins from prokaryotic organisms (i.e., superkingdoms Archaea and Bacteria) with respect to functional categories they belong to, i.e., Clusters of Orthologous Groups of proteins (COGs) and groups of COGs-Cellular processes (Cp), Information storage and processing (Isp), Metabolism (Me) and Poorly characterized (Pc).We also analyzed the disorder content of proteins with respect to various genomic, metabolic and ecological characteristics of the organism they belong to. We used correlations and association rule mining in order to identify the most confident associations between specific modalities of the characteristics considered and disorder content.ResultsBacteria are shown to have a somewhat higher level of protein disorder than archaea, except for proteins in the Me functional group. It is demonstrated that the Isp and Cp functional groups in particular (L-repair function and N-cell motility and secretion COGs of proteins in specific) possess the highest disorder content, while Me proteins, in general, posses the lowest. Disorder fractions have been confirmed to have the lowest level for the so-called order-promoting amino acids and the highest level for the so-called disorder promoters.For each pair of organism characteristics, specific modalities are identified with the maximum disorder proteins in the corresponding organisms, e.g., high genome size-high GC content organisms, facultative anaerobic-low GC content organisms, aerobic-high genome size organisms, etc. Maximum disorder in archaea is observed for high GC content-low genome size organisms, high GC content-facultative anaerobic or aquatic or mesophilic organisms, etc. Maximum disorder in bacteria is observed for high GC content-high genome size organisms, high genome size-aerobic organisms, etc.Some of the most reliable association rules mined establish relationships between high GC content and high protein disorder, medium GC content and both medium and low protein disorder, anaerobic organisms and medium protein disorder, Gammaproteobacteria and low protein disorder, etc. A web site Prokaryote Disorder Database has been designed and implemented at the address http://bioinfo.matf.bg.ac.rs/disorder, which contains complete results of the analysis of protein disorder performed for 296 prokaryotic completely sequenced genomes.ConclusionsExhaustive disorder analysis has been performed by functional classes of proteins, for a larger dataset of prokaryotic organisms than previously done. Results obtained are well correlated to those previously published, with some extension in the range of disorder level and clear distinction between functional classes of proteins. Wide correlation and association analysis between protein disorder and genomic and ecological characteristics has been performed for the first time. The results obtained give insight into multi-relationships among the characteristics and protein disorder. Such analysis provides for better understanding of the evolutionary process and may be useful for taxon determination. The main drawback of the approach is the fact that the disorder considered has been predicted and not experimentally established.