Housing fuzzy recommender system: A systematic literature review.

In recent years, significant attention has been paid to fuzzy recommender systems for housing, highlighting their ability to effectively handle the imprecision and uncertainty inherent in the real estate market. With the objective of improving the filtering of recommendations in the real estate sector, the PRISMA 2020 methodology was applied to perform new systematic reviews using its checklist on six academic databases from 1985 to 2024. RawGraph, Orange Data Minig, Jamovi and R software were used for document classification and data visualization. After classification, 1003 articles were obtained, of which 46.36% were in Scopus, and 57.82% were articles. At the end of the type, 50 articles were identified as primary, subjecting them to six research questions. It was found that 65% of the algorithms used fuzzy logic, 60% used spatial data, and 80% evaluated performance. The main difficulties were related to the integration of various sources of information. Although incorporating reclusive methods is anticipated in future systems, the need remains to address challenging areas to improve the overall performance of fuzzy recommender systems. The reviewed articles focus on enhancing fuzzy data-based recommendation systems by proposing flexible and less intrusive techniques. The significance of incorporating contextual information and exploring hybrid approaches is emphasized, along with the evaluation in real world environments, averaging artificial intelligence.

A deep learning model for Alzheimer's disease diagnosis based on patient clinical records.

BACKGROUND: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning is a promising tool to minimize the impact of under-diagnosis. OBJECTIVE: To develop a Deep Learning model that uses clinical data from patients with dementia to classify whether they have AD. METHODS: A Deep Learning model to identify AD in clinical records is proposed. In addition, several rebalancing methods have been used to preprocess the dataset and several studies have been carried out to tune up the model. RESULTS: Model has been tested against other well-established machine learning techniques, having better results than these in terms of AUC with alpha less than 0.05. CONCLUSIONS: The developed Neural Network Model has a good performance and can be an accurate assisting tool for AD diagnosis.

Altered splicing machinery in lung carcinoids unveils NOVA1, PRPF8 and SRSF10 as novel candidates to understand tumor biology and expand biomarker discovery.

BACKGROUND: Lung neuroendocrine neoplasms (LungNENs) comprise a heterogeneous group of tumors ranging from indolent lesions with good prognosis to highly aggressive cancers. Carcinoids are the rarest LungNENs, display low to intermediate malignancy and may be surgically managed, but show resistance to radiotherapy/chemotherapy in case of metastasis. Molecular profiling is providing new information to understand lung carcinoids, but its clinical value is still limited. Altered alternative splicing is emerging as a novel cancer hallmark unveiling a highly informative layer. METHODS: We primarily examined the status of the splicing machinery in lung carcinoids, by assessing the expression profile of the core spliceosome components and selected splicing factors in a cohort of 25 carcinoids using a microfluidic array. Results were validated in an external set of 51 samples. Dysregulation of splicing variants was further explored in silico in a separate set of 18 atypical carcinoids. Selected altered factors were tested by immunohistochemistry, their associations with clinical features were assessed and their putative functional roles were evaluated in vitro in two lung carcinoid-derived cell lines. RESULTS: The expression profile of the splicing machinery was profoundly dysregulated. Clustering and classification analyses highlighted five splicing factors: NOVA1, SRSF1, SRSF10, SRSF9 and PRPF8. Anatomopathological analysis showed protein differences in the presence of NOVA1, PRPF8 and SRSF10 in tumor versus non-tumor tissue. Expression levels of each of these factors were differentially related to distinct number and profiles of splicing events, and were associated to both common and disparate functional pathways. Accordingly, modulating the expression of NOVA1, PRPF8 and SRSF10 in vitro predictably influenced cell proliferation and colony formation, supporting their functional relevance and potential as actionable targets. CONCLUSIONS: These results provide primary evidence for dysregulation of the splicing machinery in lung carcinoids and suggest a plausible functional role and therapeutic targetability of NOVA1, PRPF8 and SRSF10.

Progressive growing of Generative Adversarial Networks for improving data augmentation and skin cancer diagnosis.

Early melanoma diagnosis is the most important factor in the treatment of skin cancer and can effectively reduce mortality rates. Recently, Generative Adversarial Networks have been used to augment data, prevent overfitting and improve the diagnostic capacity of models. However, its application remains a challenging task due to the high levels of inter and intra-class variance seen in skin images, limited amounts of data, and model instability. We present a more robust Progressive Growing of Adversarial Networks based on residual learning, which is highly recommended to ease the training of deep networks. The stability of the training process was increased by receiving additional inputs from preceding blocks. The architecture is able to produce plausible photorealistic synthetic 512 × 512 skin images, even with small dermoscopic and non-dermoscopic skin image datasets as problem domains. In this manner, we tackle the lack of data and the imbalance problems. Additionally, the proposed approach leverages a skin lesion boundary segmentation algorithm and transfer learning to enhance the diagnosis of melanoma. Inception score and Matthews Correlation Coefficient were used to measure the performance of the models. The architecture was evaluated qualitatively and quantitatively through the use of an extensive experimental study on sixteen datasets, illustrating its effectiveness in the diagnosis of melanoma. Finally, four state-of-the-art data augmentation techniques applied in five convolutional neural network models were significantly outperformed. The results indicated that a bigger number of trainable parameters will not necessarily obtain a better performance in melanoma diagnosis.

Data mining techniques for predicting teacher evaluation in higher education: A systematic literature review.

Teacher evaluation is presented as an object of study of great interest, where multiple efforts converge to establish models from the association of heterogeneous data from academic actors, one of these is the students' community, who stands out for their contribution with rich data information for the establishment of teacher evaluation in higher education. This study aims to present the search results for references on the prediction of teacher evaluation based on the associated data provided by the performance of university students. For this purpose, a systematic literature review was carried out, established by the phases of planning (search objective, research questions, inclusion and exclusion criteria), search and selection (literature control group and keywords, the definition of the search string, results filtering), and extraction (synthesis of the contributions). As a result, a set of references on the application of predictions is obtained, focused on educational data mining techniques, such as Fuzzy logic, Fuzzy clustering, Fuzzy Neural Network (FNN), Neural networks, multilayer perceptron (MLP), Decision Trees, Logistic Regression, Random Forest Classifier, Naïve Bayes Classifier, Support Vector Machine (SVM), K-Nearest-Neighbor (KNN), and Associative classification model. In conclusion, prediction and mining techniques have been widely explored; however, teacher evaluation is in the process of growth with particular emphasis on fuzzy principles, considering that human decision-making is developed with uncertainty, which is strongly related to human behavior.

Spliceosomic dysregulation unveils NOVA1 as a candidate actionable therapeutic target in pancreatic neuroendocrine tumors.

Dysregulation of the splicing machinery is emerging as a hallmark in cancer due to its association with multiple dysfunctions in tumor cells. Inappropriate function of this machinery can generate tumor-driving splicing variants and trigger oncogenic actions. However, its role in pancreatic neuroendocrine tumors (PanNETs) is poorly defined. In this study we aimed to characterize the expression pattern of a set of splicing machinery components in PanNETs, and their relationship with aggressiveness features. A qPCR-based array was first deployed to determine the expression levels of components of the major (n = 13) and minor spliceosome (n = 4) and associated splicing factors (n = 27), using a microfluidic technology in 20 PanNETs and non-tumoral adjacent samples. Subsequently, in vivo and in vitro models were applied to explore the pathophysiological role of NOVA1. Expression analysis revealed that a substantial proportion of splicing machinery components was altered in tumors. Notably, key splicing factors were overexpressed in PanNETs samples, wherein their levels correlated with clinical and malignancy features. Using in vivo and in vitro assays, we demonstrate that one of those altered factors, NOVA1, is tightly related to cell proliferation, alters pivotal signaling pathways and interferes with responsiveness to drug treatment in PanNETs, suggesting a role for this factor in the aggressiveness of these tumors and its suitability as therapeutic target. Altogether, our results unveil a severe alteration of the splicing machinery in PanNETs and identify the putative relevance of NOVA1 in tumor development/progression, which could provide novel avenues to develop diagnostic biomarkers and therapeutic tools for this pathology.

Dysregulated splicing factor SF3B1 unveils a dual therapeutic vulnerability to target pancreatic cancer cells and cancer stem cells with an anti-splicing drug.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, requiring novel treatments to target both cancer cells and cancer stem cells (CSCs). Altered splicing is emerging as both a novel cancer hallmark and an attractive therapeutic target. The core splicing factor SF3B1 is heavily altered in cancer and can be inhibited by Pladienolide-B, but its actionability in PDAC is unknown. We explored the presence and role of SF3B1 in PDAC and interrogated its potential as an actionable target. METHODS: SF3B1 was analyzed in PDAC tissues, an RNA-seq dataset, and publicly available databases, examining associations with splicing alterations and key features/genes. Functional assays in PDAC cell lines and PDX-derived CSCs served to test Pladienolide-B treatment effects in vitro, and in vivo in zebrafish and mice. RESULTS: SF3B1 was overexpressed in human PDAC and associated with tumor grade and lymph-node involvement. SF3B1 levels closely associated with distinct splicing event profiles and expression of key PDAC players (KRAS, TP53). In PDAC cells, Pladienolide-B increased apoptosis and decreased multiple tumor-related features, including cell proliferation, migration, and colony/sphere formation, altering AKT and JNK signaling, and favoring proapoptotic splicing variants (BCL-XS/BCL-XL, KRASa/KRAS, Δ133TP53/TP53). Importantly, Pladienolide-B similarly impaired CSCs, reducing their stemness capacity and increasing their sensitivity to chemotherapy. Pladienolide-B also reduced PDAC/CSCs xenograft tumor growth in vivo in zebrafish and in mice. CONCLUSION: SF3B1 overexpression represents a therapeutic vulnerability in PDAC, as altered splicing can be targeted with Pladienolide-B both in cancer cells and CSCs, paving the way for novel therapies for this lethal cancer.

Melanoma Recognition by Fusing Convolutional Blocks and Dynamic Routing between Capsules.

Skin cancer is one of the most common types of cancers in the world, with melanoma being the most lethal form. Automatic melanoma diagnosis from skin images has recently gained attention within the machine learning community, due to the complexity involved. In the past few years, convolutional neural network models have been commonly used to approach this issue. This type of model, however, presents disadvantages that sometimes hamper its application in real-world situations, e.g., the construction of transformation-invariant models and their inability to consider spatial hierarchies between entities within an image. Recently, Dynamic Routing between Capsules architecture (CapsNet) has been proposed to overcome such limitations. This work is aimed at proposing a new architecture which combines convolutional blocks with a customized CapsNet architecture, allowing for the extraction of richer abstract features. This architecture uses high-quality 299×299×3 skin lesion images, and a hyper-tuning of the main parameters is performed in order to ensure effective learning under limited training data. An extensive experimental study on eleven image datasets was conducted where the proposal significantly outperformed several state-of-the-art models. Finally, predictions made by the model were validated through the application of two modern model-agnostic interpretation tools.

CRBA: A Competitive Rate-Based Algorithm Based on Competitive Spiking Neural Networks.

In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA's performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation.

Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach.

BACKGROUND: The dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology. OBJECTIVE: The aim of this study was to reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. METHODS: We built predictive models using different subsets of factors, selected according to their importance in predicting patient classification. We then evaluated each independent model and also a combination of them, leading to a better predictive model. RESULTS: Our data mining approach identified genetic patterns that escaped detection using conventional statistics. More specifically, the partial decision trees and ensemble models increased the classification accuracy of hepatitis C virus outcome compared with conventional methods. CONCLUSIONS: Data mining can be used more extensively in biomedicine, facilitating knowledge building and management of human diseases.

Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study.

Melanoma is the type of skin cancer with the highest levels of mortality, and it is more dangerous because it can spread to other parts of the body if not caught and treated early. Melanoma diagnosis is a complex task, even for expert dermatologists, mainly due to the great variety of morphologies in moles of patients. Accordingly, the automatic diagnosis of melanoma is a task that poses the challenge of developing efficient computational methods that ease the diagnostic and, therefore, aid dermatologists in decision-making. In this work, an extensive analysis was conducted, aiming at assessing and illustrating the effectiveness of convolutional neural networks in coping with this complex task. To achieve this objective, twelve well-known convolutional network models were evaluated on eleven public image datasets. The experimental study comprised five phases, where first it was analyzed the sensitivity of the models regarding the optimization algorithm used for their training, and then it was analyzed the impact in performance when using different techniques such as cost-sensitive learning, data augmentation and transfer learning. The conducted study confirmed the usefulness, effectiveness and robustness of different convolutional architectures in solving melanoma diagnosis problem. Also, important guidelines to researchers working on this area were provided, easing the selection of both the proper convolutional model and technique according the characteristics of data.

Splicing machinery dysregulation drives glioblastoma development/aggressiveness: oncogenic role of SRSF3.

Glioblastomas remain the deadliest brain tumour, with a dismal ∼12-16-month survival from diagnosis. Therefore, identification of new diagnostic, prognostic and therapeutic tools to tackle glioblastomas is urgently needed. Emerging evidence indicates that the cellular machinery controlling the splicing process (spliceosome) is altered in tumours, leading to oncogenic splicing events associated with tumour progression and aggressiveness. Here, we identify for the first time a profound dysregulation in the expression of relevant spliceosome components and splicing factors (at mRNA and protein levels) in well characterized cohorts of human high-grade astrocytomas, mostly glioblastomas, compared to healthy brain control samples, being SRSF3, RBM22, PTBP1 and RBM3 able to perfectly discriminate between tumours and control samples, and between proneural-like or mesenchymal-like tumours versus control samples from different mouse models with gliomas. Results were confirmed in four additional and independent human cohorts. Silencing of SRSF3, RBM22, PTBP1 and RBM3 decreased aggressiveness parameters in vitro (e.g. proliferation, migration, tumorsphere-formation, etc.) and induced apoptosis, especially SRSF3. Remarkably, SRSF3 was correlated with patient survival and relevant tumour markers, and its silencing in vivo drastically decreased tumour development and progression, likely through a molecular/cellular mechanism involving PDGFRB and associated oncogenic signalling pathways (PI3K-AKT/ERK), which may also involve the distinct alteration of alternative splicing events of specific transcription factors controlling PDGFRB (i.e. TP73). Altogether, our results demonstrate a drastic splicing machinery-associated molecular dysregulation in glioblastomas, which could potentially be considered as a source of novel diagnostic and prognostic biomarkers as well as therapeutic targets for glioblastomas. Remarkably, SRSF3 is directly associated with glioblastoma development, progression, aggressiveness and patient survival and represents a novel potential therapeutic target to tackle this devastating pathology.

A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis.

Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not a straightforward task mainly due to the heterogeneity of tumors, the variability across samples, and the fat-short characteristic of genomic datasets. In this work, a supervised machine learning-based methodology is proposed, allowing the determination of subsets of relevant splicing components that best discriminate samples. The methodology comprises three main phases: first, a ranking of features is determined by means of applying feature weighting algorithms that compute the importance of each splicing component; second, the best subset of features that allows the induction of an accurate classifier is determined by means of conducting an effective heuristic search; then the confidence over the induced classifier is assessed by means of explaining the individual predictions and its global behavior. At the end, an extensive experimental study was conducted on a large collection of transcript-based datasets, illustrating the utility and benefit of the proposed methodology for analyzing dysregulation in splicing machinery.

Dysregulation of the splicing machinery is directly associated to aggressiveness of prostate cancer.

BACKGROUND: Dysregulation of splicing variants (SVs) expression has recently emerged as a novel cancer hallmark. Although the generation of aberrant SVs (e.g. AR-v7/sst5TMD4/etc.) is associated to prostate-cancer (PCa) aggressiveness and/or castration-resistant PCa (CRPC) development, whether the molecular reason behind such phenomena might be linked to a dysregulation of the cellular machinery responsible for the splicing process [spliceosome-components (SCs) and splicing-factors (SFs)] has not been yet explored. METHODS: Expression levels of 43 key SCs and SFs were measured in two cohorts of PCa-samples: 1) Clinically-localized formalin-fixed paraffin-embedded PCa-samples (n = 84), and 2) highly-aggressive freshly-obtained PCa-samples (n = 42). FINDINGS: A profound dysregulation in the expression of multiple components of the splicing machinery (i.e. 7 SCs/19 SFs) were found in PCa compared to their non-tumor adjacent-regions. Notably, overexpression of SNRNP200, SRSF3 and SRRM1 (mRNA and/or protein) were associated with relevant clinical (e.g. Gleason score, T-Stage, metastasis, biochemical recurrence, etc.) and molecular (e.g. AR-v7 expression) parameters of aggressiveness in PCa-samples. Functional (cell-proliferation/migration) and mechanistic [gene-expression (qPCR) and protein-levels (western-blot)] assays were performed in normal prostate cells (PNT2) and PCa-cells (LNCaP/22Rv1/PC-3/DU145 cell-lines) in response to SNRNP200, SRSF3 and/or SRRM1 silencing (using specific siRNAs) revealed an overall decrease in proliferation/migration-rate in PCa-cells through the modulation of key oncogenic SVs expression levels (e.g. AR-v7/PKM2/XBP1s) and alteration of oncogenic signaling pathways (e.g. p-AKT/p-JNK). INTERPRETATION: These results demonstrate that the spliceosome is drastically altered in PCa wherein SNRNP200, SRSF3 and SRRM1 could represent attractive novel diagnostic/prognostic and therapeutic targets for PCa and CRPC.

Distributed Selection of Continuous Features in Multilabel Classification Using Mutual Information.

Multilabel learning is a challenging task demanding scalable methods for large-scale data. Feature selection has shown to improve multilabel accuracy while defying the curse of dimensionality of high-dimensional scattered data. However, the increasing complexity of multilabel feature selection, especially on continuous features, requires new approaches to manage data effectively and efficiently in distributed computing environments. This article proposes a distributed model for mutual information (MI) adaptation on continuous features and multiple labels on Apache Spark. Two approaches are presented based on MI maximization, and minimum redundancy and maximum relevance. The former selects the subset of features that maximize the MI between the features and the labels, whereas the latter additionally minimizes the redundancy between the features. Experiments compare the distributed multilabel feature selection methods on 10 data sets and 12 metrics. Results validated through statistical analysis indicate that our methods outperform reference methods for distributed feature selection for multilabel data, while MIM also reduces the runtime in orders of magnitude.

Performing Multi-Target Regression via a Parameter Sharing-Based Deep Network.

Multi-target regression (MTR) comprises the prediction of multiple continuous target variables from a common set of input variables. There are two major challenges when addressing the MTR problem: the exploration of the inter-target dependencies and the modeling of complex input-output relationships. This paper proposes a neural network model that is able to simultaneously address these two challenges in a flexible way. A deep architecture well suited for learning multiple continuous outputs is designed, providing some flexibility to model the inter-target relationships by sharing network parameters as well as the possibility to exploit target-specific patterns by learning a set of nonshared parameters for each target. The effectiveness of the proposal is analyzed through an extensive experimental study on 18 datasets, demonstrating the benefits of using a shared representation that exploits the commonalities between target variables. According to the experimental results, the proposed model is competitive with respect to the state-of-the-art in MTR.

Dysregulation of the Splicing Machinery Is Associated to the Development of Nonalcoholic Fatty Liver Disease.

CONTEXT: Nonalcoholic fatty liver disease (NAFLD) is a common obesity-associated pathology characterized by hepatic fat accumulation, which can progress to fibrosis, cirrhosis, and hepatocellular carcinoma. Obesity is associated with profound changes in gene-expression patterns of the liver, which could contribute to the onset of comorbidities. OBJECTIVE: As these alterations might be linked to a dysregulation of the splicing process, we aimed to determine whether the dysregulation in the expression of splicing machinery components could be associated with NAFLD. PARTICIPANTS: We collected 41 liver biopsies from nonalcoholic individuals with obesity, with or without hepatic steatosis, who underwent bariatric surgery. INTERVENTIONS: The expression pattern of splicing machinery components was determined using a microfluidic quantitative PCR-based array. An in vitro approximation to determine lipid accumulation using HepG2 cells was also implemented. RESULTS: The liver of patients with obesity and steatosis exhibited a severe dysregulation of certain splicing machinery components compared with patients with obesity without steatosis. Nonsupervised clustering analysis allowed the identification of three molecular phenotypes of NAFLD with a unique fingerprint of alterations in splicing machinery components, which also presented distinctive hepatic and clinical-metabolic alterations and a differential response to bariatric surgery after 1 year. In addition, in vitro silencing of certain splicing machinery components (i.e., PTBP1, RBM45, SND1) reduced fat accumulation and modulated the expression of key de novo lipogenesis enzymes, whereas conversely, fat accumulation did not alter spliceosome components expression. CONCLUSION: There is a close relationship between splicing machinery dysregulation and NAFLD development, which should be further investigated to identify alternative therapeutic targets.

Changes in Splicing Machinery Components Influence, Precede, and Early Predict the Development of Type 2 Diabetes: From the CORDIOPREV Study.

BACKGROUND: Type-2 diabetes mellitus (T2DM) is a major health problem with increasing incidence, which severely impacts cardiovascular disease. Because T2DM is associated with altered gene expression and aberrant splicing, we hypothesized that dysregulations in splicing machinery could precede, contribute to, and predict T2DM development. METHODS: A cohort of patients with cardiovascular disease (CORDIOPREV study) and without T2DM at baseline (at the inclusion of the study) was used (n = 215). We determined the expression of selected splicing machinery components in fasting and 4 h-postprandial peripheral blood mononuclear cells (PBMCs, obtained at baseline) from all the patients who developed T2DM during 5-years of follow-up (n = 107 incident-T2DM cases) and 108 randomly selected non-T2DM patients (controls). Serum from incident-T2DM and control patients was used to analyze in vitro the modulation of splicing machinery expression in control PBMCs from an independent cohort of healthy subjects. FINDINGS: Expression of key splicing machinery components (e.g. RNU2, RNU4 or RNU12) from fasting and 4 h-postprandial PBMCs of incident-T2DM patients was markedly altered compared to non-T2DM controls. Moreover, in vitro treatment of healthy individuals PBMCs with serum from incident-T2DM patients (compared to non-T2DM controls) reduced the expression of splicing machinery elements found down-regulated in incident-T2DM patients PBMCs. Finally, fasting/postprandial levels of several splicing machinery components in the PBMCs of CORDIOPREV patients were associated to higher risk of T2DM (Odds Ratio > 4) and could accurately predict (AUC > 0.85) T2DM development. INTERPRETATION: Our results reveal the existence of splicing machinery alterations that precede and predict T2DM development in patients with cardiovascular disease. FUND: ISCIII, MINECO, CIBERObn.

Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data.

Pattern mining is one of the most important tasks to extract meaningful and useful information from raw data. This task aims to extract item-sets that represent any type of homogeneity and regularity in data. Although many efficient algorithms have been developed in this regard, the growing interest in data has caused the performance of existing pattern mining techniques to be dropped. The goal of this paper is to propose new efficient pattern mining algorithms to work in big data. To this aim, a series of algorithms based on the MapReduce framework and the Hadoop open-source implementation have been proposed. The proposed algorithms can be divided into three main groups. First, two algorithms [Apriori MapReduce (AprioriMR) and iterative AprioriMR] with no pruning strategy are proposed, which extract any existing itemset in data. Second, two algorithms (space pruning AprioriMR and top AprioriMR) that prune the search space by means of the well-known anti-monotone property are proposed. Finally, a last algorithm (maximal AprioriMR) is also proposed for mining condensed representations of frequent patterns. To test the performance of the proposed algorithms, a varied collection of big data datasets have been considered, comprising up to 3·1018 transactions and more than 5 million of distinct single-items. The experimental stage includes comparisons against highly efficient and well-known pattern mining algorithms. Results reveal the interest of applying MapReduce versions when complex problems are considered, and also the unsuitability of this paradigm when dealing with small data.

Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming.

Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.