A New Approach for Mining Correlated Frequent Subgraphs

2022 ◽  
Vol 13 (1) ◽  
pp. 1-28
Author(s):  
Mohammad Ehsan Shahmi Chowdhury ◽  
Chowdhury Farhan Ahmed ◽  
Carson K. Leung
Keyword(s):  
Correlation Analysis ◽  
Real World ◽  
Graph Mining ◽  
Real Life ◽  
Small Subset ◽  
Method Evaluation ◽  
New Approach ◽  
Frequent Subgraph ◽  
Frequent Subgraphs ◽  
Complete Framework

Nowadays graphical datasets are having a vast amount of applications. As a result, graph mining—mining graph datasets to extract frequent subgraphs—has proven to be crucial in numerous aspects. It is important to perform correlation analysis among the subparts (i.e., elements) of the frequent subgraphs generated using graph mining to observe interesting information. However, the majority of existing works focuses on complexities in dealing with graphical structures, and not much work aims to perform correlation analysis. For instance, a previous work realized in this regard, operated with a very naive raw approach to fulfill the objective, but dealt only on a small subset of the problem. Hence, in this article, a new measure is proposed to aid in the analysis for large subgraphs, mined from various types of graph transactions in the dataset. These subgraphs are immense in terms of their structural composition, and thus parallel the entire set of graphs in real-world. A complete framework for discovering the relations among parts of a frequent subgraph is proposed using our new method. Evaluation results show the usefulness and accuracy of the newly defined measure on real-life graphical datasets.

scholarly journals A qualitative survey on frequent subgraph mining

2018 ◽  
Vol 8 (1) ◽  
pp. 194-209 ◽  
Author(s):  
Büsra Güvenoglu ◽  
Belgin Ergenç Bostanoglu
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Research Area ◽  
Heterogeneous Data ◽  
Graph Representation ◽  
Frequent Subgraph Mining ◽  
Subgraph Mining ◽  
Frequent Subgraph ◽  
Input Type ◽  
Frequent Subgraphs

AbstractData mining is a popular research area that has been studied by many researchers and focuses on finding unforeseen and important information in large databases. One of the popular data structures used to represent large heterogeneous data in the field of data mining is graphs. So, graph mining is one of the most popular subdivisions of data mining. Subgraphs that are more frequently encountered than the user-defined threshold in a database are called frequent subgraphs. Frequent subgraphs in a database can give important information about this database. Using this information, data can be classified, clustered and indexed. The purpose of this survey is to examine frequent subgraph mining algorithms (i) in terms of frequent subgraph discovery process phases such as candidate generation and frequency calculation, (ii) categorize the algorithms according to their general attributes such as input type, dynamicity of graphs, result type, algorithmic approach they are based on, algorithmic design and graph representation as well as (iii) to discuss the performance of algorithms in comparison to each other and the challenges faced by the algorithms recently.

scholarly journals A New Approach for Optimization of Real Life Transportation Problem in Neutrosophic Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
A. Thamaraiselvi ◽  
R. Santhi
Keyword(s):  
Fuzzy Sets ◽  
Real World ◽  
Transportation Problem ◽  
Real Life ◽  
Intuitionistic Fuzzy Sets ◽  
Mathematical Representation ◽  
Neutrosophic Sets ◽  
New Approach ◽  
Real World Problem ◽  
First Time

Neutrosophic sets have been introduced as a generalization of crisp sets, fuzzy sets, and intuitionistic fuzzy sets to represent uncertain, inconsistent, and incomplete information about a real world problem. For the first time, this paper attempts to introduce the mathematical representation of a transportation problem in neutrosophic environment. The necessity of the model is discussed. A new method for solving transportation problem with indeterminate and inconsistent information is proposed briefly. A real life example is given to illustrate the efficiency of the proposed method in neutrosophic approach.

scholarly journals A survey of frequent subgraph mining algorithms

2012 ◽  
Vol 28 (1) ◽  
pp. 75-105 ◽  
Author(s):  
Chuntao Jiang ◽  
Frans Coenen ◽  
Michele Zito
Keyword(s):  
Graph Mining ◽  
Research Area ◽  
Data Sets ◽  
Frequent Subgraph Mining ◽  
Computationally Efficient ◽  
Research Issues ◽  
Main Research ◽  
Subgraph Mining ◽  
Frequent Subgraph ◽  
Frequent Subgraphs

AbstractGraph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining and proposes solutions to address the main research issues.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

scholarly journals POS1121 DEMOGRAPHICS, COMORBIDITIES, AND RENAL FUNCTION OF UNCONTROLLED GOUT PATIENTS WHO RECEIVED PEGLOTICASE: FINDING FROM A LARGE US CLAIMS DATABASE

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 839.2-840
Author(s):  
C. Vesel ◽  
A. Morton ◽  
M. Francis-Sedlak ◽  
B. Lamoreaux
Keyword(s):  
Kidney Function ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Small Subset ◽  
Phase 3 ◽  
Claims Database ◽  
Medical Diagnoses ◽  
Ckd Stage ◽  
Before And After ◽  
The Impact

Background:NHANES data indicate that approximately 9.2 million Americans have gout,1 with a small subset having uncontrolled disease.2 Pegloticase is a PEGylated recombinant uricase enzyme indicated for treating uncontrolled gout that markedly reduces serum uric acid levels (sUA)3 and resolves tophi in treatment responders.4 Despite pegloticase availability in the US for many years, real world demographics of pegloticase users in the treatment of uncontrolled gout have not been previously reported in a population-based cohort.Objectives:This study utilized a large US claims database to examine demographics and co-morbidities of uncontrolled gout patients treated with pegloticase. Kidney function before and after pegloticase treatment and concomitant therapy with immunomodulators were also examined.Methods:The TriNetX Diamond database includes de-identified data from 4.3 million US patients with gout (as of September 2019), including demographics, medical diagnoses, laboratory values, procedures (e.g. infusions, surgeries), and pharmacy data. Patients who had received ≥1 pegloticase infusion were included in these analyses. The number of infusions was evaluated for a subgroup of patients who were in the database ≥3 months before and ≥2 years after the first pegloticase infusion (i.e. first infusion prior to September 2017) to ensure only complete courses of therapy were captured. In this subpopulation, kidney function before and after pegloticase therapy was examined, along with the presence of immunomodulation prescriptions (methotrexate, mycophenolate mofetil, azathioprine, leflunomide) within 60 days prior to and 14 days after the first pegloticase infusion.Results:1494 patients treated with pegloticase were identified. Patients were 63.1 ± 14.0 years of age (range: 23–91), mostly male (82%), and white (76%). Mean sUA prior to pegloticase was 8.7 ± 2.4 mg/dL (n=50), indicating uncontrolled gout in the identified population. The most commonly reported comorbidities were chronic kidney disease (CKD, 48%), essential hypertension (71%), type 2 diabetes (39%), and cardiovascular disease (38%), similar to pegloticase pivotal Phase 3 trial populations. In patients with pre-therapy kidney function measures (n=134), pre-treatment eGFR averaged 61.2 ± 25.7 ml/min/1.73 m2, with 44% having Stage 3-5 CKD. In patients with complete therapy course capture and pre- and post-therapy eGFR measures (n=48), kidney function remained stable (change in eGFR: -2.9 ± 18.2 ml/min/1.73 m2) and CKD stage remained the same or improved in 81% of patients. In 791 patients with complete treatment course capture, patients had received 8.7 ± 13.8 infusions (median: 3, IQR: 2-10). Of these, 189 (24%) patients received only 1 pegloticase infusion and 173 (22%) received ≥12 infusions. As the data cut-off for this analysis pre-dated emerging data on the use of immunomodulation as co-therapy, only 19 of 791 (2%) patients received immunomodulation co-therapy with pegloticase.Conclusion:This relatively large group of patients with uncontrolled gout treated with pegloticase had similar patient characteristics of those studied in the phase 3 randomized clinical trials. Patients with uncontrolled gout are significantly burdened with systemic co-morbid diseases. The majority of patients had stable or improved kidney function following pegloticase treatment. As these results reflect patients initiating treatment prior to 2018, before co-treatment with immunomodulation was introduced, this cohort only included a small percentage of patients who were co-treated with an immunomodulator. Future studies using more current datasets are needed to evaluate real world outcomes in patients treated with pegloticase/immunomodulator co-therapy and to evaluate the impact of systemic co-morbid diseases.References:[1]Chen-Xu M, et al. Arthritis Rheumatol 2019 71:991-999.[2]Fels E, Sundy JS. Curr Opin Rheumatol 2008;20:198-202.[3]Sundy J, et al. JAMA 2011;306:711-720.[4]Mandell BF, et al. Arthritis Res Ther 2018;20:286.Disclosure of Interests:Claudia Vesel Shareholder of: Horizon Therapeutics plc, Employee of: Horizon Therapeutics plc, Allan Morton Speakers bureau: Sanofi, Amgen, and Horizon, Megan Francis-Sedlak Shareholder of: Horizon Therapeutics plc, Employee of: Horizon Therapeutics plc, Brian LaMoreaux Shareholder of: Horizon Therapeutics plc, Employee of: Horizon Therapeutics plc.

scholarly journals Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

2021 ◽  
Author(s):  
Amarildo Likmeta ◽  
Alberto Maria Metelli ◽  
Giorgia Ramponi ◽  
Andrea Tirinzoni ◽  
Matteo Giuliani ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Real Life ◽  
User Preferences ◽  
Inverse Reinforcement Learning ◽  
Water Release ◽  
Reward Function ◽  
Model Free ◽  
Conflicting Objectives ◽  
Multiple Experts

AbstractIn real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

scholarly journals Improving the performance of a radio-frequency localization system in adverse outdoor applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Marcelo N. de Sousa ◽  
Ricardo Sant’Ana ◽  
Rigel P. Fernandes ◽  
Julio Cesar Duarte ◽  
José A. Apolinário ◽  
...  
Keyword(s):  
Random Forest ◽  
Ray Tracing ◽  
Real World ◽  
Practical Implication ◽  
Real Life ◽  
Simulated Data ◽  
Real Data ◽  
Gradient Boosting ◽  
Real World Data ◽  
Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

scholarly journals An Adaptive Game-Based Learning Strategy for Children Road Safety Education and Practice in Virtual Space

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3661
Author(s):  
Noman Khan ◽  
Khan Muhammad ◽  
Tanveer Hussain ◽  
Mansoor Nasir ◽  
Muhammad Munsif ◽  
...  
Keyword(s):  
Real World ◽  
Road Safety ◽  
Learning Strategy ◽  
Real Life ◽  
Safety Training ◽  
Virtual Space ◽  
Safety Education ◽  
Average Output ◽  
Training Environment ◽  
The Road

Virtual reality (VR) has been widely used as a tool to assist people by letting them learn and simulate situations that are too dangerous and risky to practice in real life, and one of these is road safety training for children. Traditional video- and presentation-based road safety training has average output results as it lacks physical practice and the involvement of children during training, without any practical testing examination to check the learned abilities of a child before their exposure to real-world environments. Therefore, in this paper, we propose a 3D realistic open-ended VR and Kinect sensor-based training setup using the Unity game engine, wherein children are educated and involved in road safety exercises. The proposed system applies the concepts of VR in a game-like setting to let the children learn about traffic rules and practice them in their homes without any risk of being exposed to the outside environment. Thus, with our interactive and immersive training environment, we aim to minimize road accidents involving children and contribute to the generic domain of healthcare. Furthermore, the proposed framework evaluates the overall performance of the students in a virtual environment (VE) to develop their road-awareness skills. To ensure safety, the proposed system has an extra examination layer for children’s abilities evaluation, whereby a child is considered fit for real-world practice in cases where they fulfil certain criteria by achieving set scores. To show the robustness and stability of the proposed system, we conduct four types of subjective activities by involving a group of ten students with average grades in their classes. The experimental results show the positive effect of the proposed system in improving the road crossing behavior of the children.

scholarly journals Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments

2019 ◽  
Vol 8 (6) ◽  
pp. 272 ◽  
Author(s):  
Iq Reviessay Pulshashi ◽  
Hyerim Bae ◽  
Hyunsuk Choi ◽  
Seunghwan Mun ◽  
Riska Asriana Sutrisnowati
Keyword(s):  
South Korea ◽  
Real World ◽  
Experimental Evaluation ◽  
Streaming Data ◽  
Harsh Environments ◽  
Noise Filtering ◽  
New Approach ◽  
Detection Algorithms ◽  
Outlying Point

Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach.

Between Real and Ideal: Documenting Media Art

Leonardo ◽  
2008 ◽  
Vol 41 (4) ◽  
pp. 418-419 ◽  
Author(s):  
Caitlin Jones ◽  
Lizzie Muller
Keyword(s):  
Data Structure ◽  
Real World ◽  
Media Art ◽  
New Approach ◽  
The Real ◽  
Key Features ◽  
The Ideal

This paper describes a new approach to documenting media art which seeks to place in dialogue the artist's intentions and the audience's experience. It explicitly highlights the productive tension between the ideal, conceptual existence of the work, and its actual manifestation through different iterations and exhibitions in the real world. The paper describes how the approach was developed collaboratively during the production of a documentary collection for the artwork Giver of Names, by David Rokeby. It outlines the key features of the approach including artist's interview, audience interviews and data structure.

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