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نتیجه جستجو - Association rule mining

تعداد مقالات یافته شده: 21
ردیف عنوان نوع
1 Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining
چارچوب های یادگیری ماشین و داده کاوی برای پیش بینی پاسخ به دارو در سرطان: یک مرور کلی و رمان در فرآیند غربالگری سیلیکون بر اساس قاعده قاچاق انجمن-2019
A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big “omic” data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges tomaximizemodel performance. In this contextwe also describe a novel in silico screening process, based on Association RuleMining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large samplespaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.
Key words: Drug Response Prediction | Precision Medicine | Data mining | Machine Learning | Association Rule Mining
مقاله انگلیسی
2 Systematic data mining-based framework to discover potential energy waste patterns in residential buildings
چارچوب مبتنی بر داده کاوی سیستمیک برای کشف الگوهای احتمالی پسماندهای انرژی در ساختمانهای مسکونی-2019
Energy feedback systems are recently proposed to help occupants understand and improve their energy use behavior. Despite many potential benefits, the question remains, whether useful and straightforward knowledge are transferred to the occupants about their energy use patterns. In this context, the key is to develop methodologies that can effectively analyze occupants’ energy use behavior and distinguish their energy-inefficient behavior (if any). Previous studies seldom considered the dynamics of occupancy, which may result in misleading information to the occupants and inefficacy in recognizing the actual wasteful behavior. To fill this gap, this study proposes a data mining framework with a combination of change point analysis (CPA), cluster analysis, and association rule mining (ARM) to explore the relation- ship between occupancy and building energy consumption, aiming at identifying potential energy waste patterns and to provide useful feedback to the occupants. To demonstrate the capability of the developed framework, it was applied to datasets collected from two different apartments located in Lyon, France. Results indicate that different energy waste patterns can be effectively discovered in both apartments through the proposed framework and a substantial amount of energy savings can be achieved by modi- fying occupants’ energy use behavior. The proposed framework is flexible and can be adaptive to house- holds with different occupancy patterns and habitual energy-use behavior. Nevertheless, the discovered energy saving potentials and benchmark values are limited to the apartments considered in this study and similar analysis based on the proposed framework are needed in wider building stocks to explore its generalizability.
Keywords: Residential buildings | Occupant behavior | Data mining | Energy savings | Feedback
مقاله انگلیسی
3 Data mining in photocatalytic water splitting over perovskites literature for higher hydrogen production
داده کاوی در تقسیم آب فوتوکاتالیستی بر ادبیات پروسکایت برای تولید هیدروژن بالاتر-2019
A database containing 540 cases from 151 published papers on photocatalytic water splitting over perovskites was constructed and analyzed using data mining tools; the factors leading high hydrogen production were identified by association rule mining while some useful heuristics for the future studies were developed by decision tree analysis. Additionally, the predictive models were developed using random forest regression. In about half of the works, the perovskites were doped by A-site, B-site or both; however, only some portion of doped catalysts had better activity than plain perovskites while doping also improved stability in some cases. The effect of co-catalyst on activity seems to be also irregular; no definitive conclusion could be drawn. The effects of preparation methods on surface area, band gap and crystal structure were noticeable. This is also observed in visible light activity; for example the materials prepared by hydrothermal synthesis method appeared to perform better under visible light. Methanol and other sacrificial agents were used in both UV and visible light tests while inorganic additives have been commonly utilized under visible light. The band gap was found to be highly predictable but it could not be directly linked to the hydrogen production. As the result, although there has been significant progress in the field, the improvement in hydrogen production appeared to be always limited; the sound solutions like ion doping to modify the band gap, use of co-catalyst for charge separation or use of additives as sacrificial agents did not to help as much as desired.
Keywords: Photocatalytic water splitting | Perovskite semiconductor | Band gap modification | Machine-learning | Data mining
مقاله انگلیسی
4 A product-centric data mining algorithm for targeted promotions
یک الگوریتم داده کاوی محصول محور برای تبلیغات هدفمند-2019
Targeted promotions in retail are becoming increasingly popular, particularly in UK grocery retail sector, where competition is stiff and consumers remain price sensitive. Given this, a targeted promotion algorithm is proposed to enhance the effectiveness of promotions by retailers. The algorithm leverages a mathematical model for optimising items to target and fuzzy c-means clustering for finding the best customers to target. Tests using simulations with real life consumer scanner panel data from the UK grocery retailer sector show that the algorithm performs well in finding the best items and customers to target whilst eliminating “false positives” (targeting customers who do not buy a product) and reducing “false negatives” (not targeting customers who could buy a product). The algorithm also shows better performance when compared to a similar published framework, particularly in handling “false positives” and “false negatives”. The paper concludes by discussing managerial and research implications, and highlights applications of the model to other fields.
Keywords: Association rule mining | Targeted marketing | Clustering
مقاله انگلیسی
5 Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining
چارچوب های یادگیری ماشین و داده کاوی برای پیش بینی پاسخ به دارو در سرطان: یک مرور کلی و رمان در فرآیند غربالگری سیلیکون بر اساس کاوش قوانین انجمنی-2019
A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big “omic” data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges tomaximizemodel performance. In this contextwe also describe a novel in silico screening process, based on Association RuleMining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large samplespaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.
Key words: Drug Response Prediction | Precision Medicine | Data mining | Machine Learning | Association Rule Mining
مقاله انگلیسی
6 Motif-based association rule mining and clustering technique for determining energy usage patterns for smart meter data
روش استخراج و مجموعه خوشه بندی قانون مبتنی بر موتیف برای تعیین الگوهای مصرف انرژی برای داده های کنتور هوشمند-2019
Nowadays, smart energy meters are being used to record periodic electricity consumption. The real time data produced by smart meters provide the detailed information about the electricity usage of a particular consumer. In this paper, we propose a motif-based association rule mining and clustering technique for determining the energy usage patterns for smart meter data. The association rules of motifs within a specific time window characterizes behaviors of energy consumer. In particular, we focus on an extraction of the temporal information of the smart meter. The process is based on the unique combination of Symbolic Aggregate approximation (SAX), temporal motif discovery and association rule mining to detect the expected and unexpected patterns robustly. Experiments on real world smart meter datasets justify that the proposed model discovers the useful routine behavior of electricity energy consumers, which are helpful for electricity utility experts. Further, in this paper, clustering on the motifs is performed which gives the different consumption behavior of consumers on different days which can help distribution network operator (DNO) for electricity network modeling and management. In future, we can form motif-based signature using the proposed approach for different applications such as anomaly detection and dynamic detection of operating patterns.
Keywords: Smart Meter | Association rule | Data analytics | Temporal data mining | Clustering Motif
مقاله انگلیسی
7 Identifying mutation positions in all segments of influenza genome enables better differentiation between pandemic and seasonal strains
شناسايي موقعيت جهش در تمامي بخش هاي ژنوم آنفلوانزا باعث تمايز بهتر ميان سويه ها و بيماري هاي فصلي مي شود-2019
Influenza has a negative sense, single-stranded, and segmented RNA. In the context of pandemic influenza research, most studies have focused on variations in the surface proteins (Hemagglutinin and Neuraminidase). However, new findings suggest that all internal and external proteins of influenza viruses can contribute in pandemic emergence, pathogenicity and increasing host range. The occurrence of the 2009 influenza pandemic and the availability of many external and internal segments of pandemic and non-pandemic sequences offer a unique opportunity to evaluate the performance of machine learning models in discrimination of pandemic from seasonal sequences using mutation positions in all segments. In this study, we hypothesized that identifying mutation positions in all segments (proteins) encoded by the influenza genome would enable pandemic and seasonal strains to be more reliably distinguished. In a large scale study, we applied a range of data mining techniques to all segments of influenza for rule discovery and discrimination of pandemic from seasonal strains. CBA (classification based on association rule mining), Ripper and Decision tree algorithms were utilized to extract association rules among mutations. CBA outperformed the other models. Our approach could discriminate pandemic sequences from seasonal ones with more than 95% accuracy for PA and NP, 99.33% accuracy for NA and 100% accuracy, precision, specificity and sensitivity (recall) for M1, M2, PB1, NS1, and NS2. The values of precision, specificity, and sensitivity were more than 90% for other segments except PB2. If sequences of all segments of one strain were available, the accuracy of discrimination of pandemic strains was 100%. General rules extracted by rule base classification approaches, such as M1-V147I, NP-N334H, NS1-V112I, and PB1-L364I, were able to detect pandemic sequences with high accuracy. We observed that mutations on internal proteins of influenza can contribute in distinguishing the pandemic viruses, similar to the external ones.
Keywords: Association rule mining | CBA | Expert system | Hot spots | Ripper algorithm | Pandemic influenza
مقاله انگلیسی
8 Study on safety mode of dragon boat sports physical fitness training based on machine learning
مطالعه نحوه ایمنی تمرینات آمادگی جسمانی ورزشی قایق اژدها بر اساس یادگیری ماشین-2019
In order to improve the safety control ability of dragon boat sports physical fitness training, this paper uses the advantages of machine learning in data analysis and feature mining in the training of dragon boat sports. A machine learning-based safety mode control model for dragon boat sports physical fitness training was proposed. Big data statistical analysis method was used to analyze the constraint parameters of the safety mode of dragon boat sports physical fitness training, and combined with the joint association rule mining method, the dragon boat sports physical fitness training safety mode training was carried out. The correlation feature quantity which constrains the safety of dragon boat physical ability training is extracted. The fuzzy clustering technique is used to classify and study the safety management data of dragon boat sport physical ability training. The method of spectral density analysis and fuzzy fusion clustering analysis is used to realize automatic mining of safety association feature data of dragon boat physical fitness training, and machine learning algorithm is combined to realize the optimization of safety pattern of dragon boat sports physical fitness training. The simulation results show that the feature extraction of the safety model of dragon boat sports physical fitness training is better and the ability of feature resolution is stronger, which improves the safety management ability of dragon boat sports physical fitness training.
Keywords: Machine learning | Dragon boat sports | Physical training | Safety mode
مقاله انگلیسی
9 A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model
یک روش کاوش قانون انجمنی معادلات داده های بزرگ برای پارامترهای ترانسفورماتور قدرت براساس مدل نمودار احتمالاتی-2018
The correlative change analysis of state parameters can provide powerful technical supports for safe, reliable, and high-efficient operation of the power transformers. However, the analysis methods are primarily based on a single or a few state parameters, and hence the potential failures can hardly be found and predicted. In this paper, a data-driven method of association rule mining for transformer state parameters has been proposed by combining the Apriori algorithm and probabilistic graphical model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations. The result indicates that association rules among the numerical solutions can be accurately mined. Finally, practical measured data of five 500 kV transformers is analyzed by the proposed method. The association rules of various state parameters have been excavated, and then the mined association rules are used in modifying the prediction results of single state parameters. The results indicate that the application of the mined association rules improves the accuracy of prediction. Therefore, the effectiveness and feasibility of the proposed method in association rule mining has been proved
Index Terms: Power transformers, state parameters, association rules, big data, data-driven method, Apriori algorithm, probabilistic graph, state prediction
مقاله انگلیسی
10 BIARAM: A process for analyzing correlated brain regions using association rule mining
BIARAM: یک فرایند برای تحلیل مناطق مرتبط مغز با استفاده از کاوش قانون انجمنی-2018
Background and objective: Because examining correlated (vs. individual) brain activity is a superior method for locating neural correlates of a stimulus, using a network approach for analyzing brain activity is gaining interest. In this study, we propose and illustrate the use of association rule mining (ARM) to analyze brain regions that are activated simultaneously. ARM is commonly used in marketing and other disciplines to help determine items that might be purchased together. We apply this technique toward identifying correlated brain regions that may respond simultaneously to specific stimuli. Our objective is to introduce ARM, describe a process for converting neural images into viable datasets (for analyses), and suggest how to apply this process for generating insights about the brain’s responses to specific stimuli (e.g. technology-associated interruptions). Methods: We analyze electroencephalogram (EEG) data collected from 46 participants; convert brain waves into images via a source localization algorithm known as sLORETA (i.e., standardized low-resolution brain electromagnetic tomography); reorganize these into a “transactional” dataset; and generate associ ation rules through ARM. Results: We compare the results with more conventional methods for analyzing neuroimaging data. We show that there is a stronger correlation between frontal lobe and sublobar/insula regions after interrup tions. This result would not be obvious from independent analysis of each region. Conclusions: The main contribution of this paper is introducing ARM as a method for analyzing multi ple images. We suggest that the biomedical community may apply this commonly available data mining technique to develop further insights about correlated regions affected by specific stimuli.
Keywords: Association rule mining ، Interruptions ، Electroencephalogram (EEG) ، Neuroimaging
مقاله انگلیسی
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