Data Mining Perspective Research Articles

Recovering nested structures in networks: an evaluation of hierarchical clustering techniques

Abstract In this article, we present various algorithms to partition the nodes of a network into groups that show the property of nestedness. Since perfect nestedness is a rare phenomenon, we consider the task from a data mining perspective, and we search for groups having high-level of nestedness. We utilize both agglomerative and divisive hierarchical clustering procedures and compare them on several benchmark and real-life networks. Furthermore, we propose different metrics derived from the results of our algorithms. We show that average-linkage and complete-linkage clustering can recover the largest fully nested clusters, and that the cluster size-weighted mean nestedness was a more stable metric for measuring clustering performance. Our proposed algorithms allow us to create multiple resolution views of nestedness-based clustering of networks, extending the field of graph-based data mining.

Hexagonal Boron Nitride Quantum Simulator: Prelude to Spin and Photonic Qubits.

The quest for qubit operation at room temperature is accelerating the field of quantum information science and technology. Solid state quantum defects with spin-optical properties are promising spin- and photonic qubit candidates for room temperature operations. In this regard, a single boron vacancy within hexagonal boron nitride (h-BN) lattice such as VB- defect has coherent quantum interfaces for spin and photonic qubits owing to the large band gap of h-BN (6 eV) that can shield a computational subspace from environmental noise. However, for a VB- defect in h-BN to be a potential quantum simulator, the design and characterization of the Hamiltonian involving mutual interactions of the defect and other degrees of freedom are needed to fully understand the effect of defects on the computational subspace. Here, we studied the key coupling tensors such as zero-field splitting, Zeeman effect, and hyperfine splitting in order to build the Hamiltonian of the VB- defect. These eigenstates are spin triplet states that form a computational subspace. To study the phonon-assisted single photon emission in the VB- defect, the Hamiltonian is characterized by electron-phonon interaction with Jahn-Teller distortions. A theoretical demonstration of how the VB- Hamiltonian is utilized to relate these quantum properties to spin- and photonic-quantum information processing. For selecting promising host 2D materials for spin and photonic qubits, we present a data-mining perspective based on the proposed Hamiltonian engineering of the VB- defect in which h-BN is one of four materials chosen to be room temperature qubit candidates.

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

Big data application and corporate investment decisions: Evidence from A-share listed companies in China

Based on a natural language processing (NLP) method and annual reports from A-share listed companies in China from 2011 to 2020, this paper constructs a big data index for the corporate level and studies the impact of big data on corporate investment decisions. The study finds that big data significantly improves corporate investment decisions. From the perspective of index decomposition, data mining and mobile service are found to significantly improve corporate investment efficiency, while algorithm development and intelligent manufacturing mainly enhance the corporate investment level. The study also notes that heterogeneity may exist due to differences in financial, innovation, and legal environments. In terms of mechanisms, big data can positively influence corporate investment decisions by improving R&D investment and reducing transaction costs.

Voices in the digital storm: Unraveling online polarization with ChatGPT

ChatGPT, an esteemed natural language processing model, has demonstrated remarkable capabilities in intelligent text generation, interactive conversation, and myriad additional tasks. The utilization of ChatGPT has generated a wide debate among users with different attitudes on social media platforms, culminating in the phenomenon of polarization. Based on confirmation bias theory, this paper presented a theoretical framework that elucidates the process of online polarization. Subsequently, we develop the sentiment classification (BERTSentiment) and topic identification (BERTopic) model leveraging the pre-trained BERT (Bidirectional Encoder Representations from Transformers) model. To empirically investigate the public sentiment regarding ChatGPT, an in-depth study was conducted on the X platform. The results indicate that although a small portion of users (approximately 10%) express negative sentiments regarding ChatGPT's ethical considerations, functionality, and accuracy, the majority of users exhibit either positive or neutral views. Among the public concerns, AI and bot functions, response quality, instant messaging, enterprise applications, and technological aspects emerge as the most prominent topics. This study sheds light on public perceptions regarding the progress and integration of emerging technologies. Moreover, it introduces a fresh data mining perspective that enhances our understanding of polarization in the context of social media research.

Clustering Social Media Data for Marketing Strategies

With the rise of social media platforms for marketing purposes, the central dilemma for researchers and policymakers lies in choosing effective data analysis tools to improve marketing decisions. In the academic literature, numerous articles have discussed clustering techniques for analysing social media data, from a perspective of data mining or social media marketing. However, few studies have attempted to synthesise results obtained from both perspectives. This research aims to (1) offer a structured overview of existing literature on clustering methods for marketing strategies and (2) compare three topic modelling techniques applied to extract the main topics evoked in the corpus of papers. Indeed, topic modelling emerges as a valuable tool for extracting relevant information from big data in general and more specifically from extensive scientific papers. Based on a thematic analysis, the extracted topics were classified according to the following categories: fields, marketing strategies and technologies. Results prove that latent Dirichlet allocation (LDA) is the most effective technique in this context. Furthermore, this study provides an overview of clustering techniques and technologies used for marketing strategies in studied fields. These findings help researchers and practitioners to select the best techniques and technologies for extracting marketing knowledge from big data.

New exploration of signal detection of Regional Risks from the perspective of data mining: a pharmacovigilance analysis based on spontaneous reporting data in Zhenjiang, China

ABSTRACT Background This study aimed to adopt the conventional signal detection methods to explore a new way of risk identification and to mine important drug risks from the perspective of big data based on Zhenjiang Adverse Event Reporting System (ZAERS). Research design and methods Data were extracted from ZAERS database between 2012 and 2022. The risks of all the reported drug event combinations were identified at the preferred term level and the standardized MedDRA query level using disproportionality analysis. Then, we conducted signal assessment according to the descriptions of drug labels. Results In total 41,473 ADE were reported and there were 12 risky signals. Signal assessment indicates the suspected causal associations in clindamycin-taste and smell disorders, valsartan-hepatic enzyme increased and valsartan-edema peripheral; the specific manifestations of allergic reactions triggered by clindamycin, cefotaxime, cefazodime, ShexiangZhuanggu plaster, ShexiangZhuifeng plaster, and Yanhuning need to be refined in drug labels. In addition, the drug labels of NiuHuangShangQing tablet/capsule, Fuyanxiao capsule, and BiYanLing tablet should be improved. Conclusions In this study, we attempted a new way to find potential drug risks using small spontaneous reporting data. Our findings also suggested the need for more precise identification of allergic risks and the improvement of traditional Chinese medicine labels.

Building information modelling-enabled multi-objective optimization for energy consumption parametric analysis in green buildings design using hybrid machine learning algorithms

Green buildings (GB) have been widely promoted in various nations. However, the post occupancy evaluation suggests many GB cannot well fulfill the expected targets. To overcome the mismatch among GB’s multi-objectives, this paper develops an efficient and intelligent hybrid method using BIM-DesignBuilder (BIM-DB), Grey wolf optimization (GWO), random forest (RF) and non-dominated sorting genetic algorithm II (NSGA-II) to achieve the optimization of design parameters. The BIM model and DB simulation tool were used to obtain data samples of envelope and air conditioning system design parameters, and their life cycle carbon emission (LCCE), economic cost (EC) and predicted mean vote (PMV). The RF model was used to achieve high precision prediction. The GWO was used in the hyper-parameter optimization. The NSGA-II algorithm was applied to multi-objective optimization to obtain optimal design parameters. A building case shows: (1) The RF model had an excellent prediction performance for LCCE, EC and PMV. (2) BIM-DB can be used to obtain low error and high reliability building simulation data sets. (3) The RF-NSGA-II intelligent algorithm can reduce the LCCE of the building in the entire cycle by 16.6%, reduce the EC per square meter by 2.0%, and greatly improve the thermal comfort by 18.3%, representing good application value. This research provides a way of thinking for the multiobjective optimization of green buildings from the perspective of data mining and guidance for the parameter selection of the envelopes and air conditioning systems of new and existing buildings to more scientifically and effectively design green buildings.

Research on learning behavior patterns from the perspective of educational data mining: Evaluation, prediction and visualization

The rapid growth of educational data creates the requirement to mine useful information from learning behavior patterns. The development of data mining technology makes educational data mining possible. The paper intends to use a public educational data set to study learning behavior patterns from the perspective of educational data mining, so as to promote the innovation of educational management. Firstly, in order to reduce the dimension of data analysis that facilitates the improvement in efficiency, principal component analysis is carried out to reduce the number of attributes in the data set. The significant attributes in the rotating principal component matrix rather than principal components which are not closely related to learning behavior patterns are extracted as the research variables. Then, a pseudo statistic is proposed to determine the number of clusters and the preprocessed data set is clustered according to the extracted attributes. The clustering results are applied to add class labels to the data, which is convenient for the later data training. Finally, six classification algorithms J48, K-Nearest Neighbor, Bayes Net, Random Forest, Support Vector Machine and Logit Boost are used to train the data with labels and build prediction models. At the same time, the performance and applicable conditions of six classifiers in terms of accuracy, efficiency, error, and so on are discussed and compared. It is found that the performance of the integrated algorithm is better than that of a single classifier. In the integrated algorithm, compared with Random Forest, the running time of Logit Boost is shorter.

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective

Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO). Given an artifact, especially a text t in question, an AA solution aims to accurately attribute t to its true author out of many candidate authors while an AO solution aims to modify t to hide its true authorship. Traditionally, the notion of authorship and its accompanying privacy concern is only toward human authors. However, in recent years, due to the explosive advancements in Neural Text Generation (NTG) techniques in NLP, capable of synthesizing human-quality openended texts (so-called "neural texts"), one has to now consider authorships by humans, machines, or their combination. Due to the implications and potential threats of neural texts when used maliciously, it has become critical to understand the limitations of traditional AA/AO solutions and develop novel AA/AO solutions in dealing with neural texts. In this survey, therefore, we make a comprehensive review of recent literature on the attribution and obfuscation of neural text authorship from a Data Mining perspective, and share our view on their limitations and promising research directions.

Fake News Prediction

This research paper aims to develop and evaluate a model for Fake news prediction using machine learning techniques. Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of news”, i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. There- fore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users’ social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

A Review on Fake News Detection using Machine Learning

Fake news, which is defined as material that has been shared with the intention of defrauding people, has been growing quickly and widely recently. This kind of misinformation is dangerous to social cohesion and wellbeing because it exacerbates political polarisation and public mistrust of authority figures. As a result, false news is an issue that has a big impact on our social lives, especially in politics. In order to address this issue, this study suggests brand-new methods based on machine learning (ML) and deep learning (DL) for the fake news identification system. This survey deals with a review of existing machine learning algorithms Naïve Bayes, Convolutional Neural Network, LSTM, Neural Network, Support Vector Machine proposed for detecting and reducing fake news from different social media platforms like Facebook, whatsapp, twitter, etc. This review provides a comprehensive detail including data mining perspective, evaluation metrics, and representative datasheets.

Genomic and immunogenomic analysis of three prognostic signature genes in LUAD

BackgroundSearching for immunotherapy-related markers is an important research content to screen for target populations suitable for immunotherapy. Prognosis-related genes in early stage lung cancer may also affect the tumor immune microenvironment, which in turn affects immunotherapy.ResultsWe analyzed the differential genes affecting lung cancer patients receiving immunotherapy through the Cancer Treatment Response gene signature DataBase (CTR-DB), and set a threshold to obtain a total of 176 differential genes between response and non-response to immunotherapy. Functional enrichment analysis found that these differential genes were mainly involved in immune regulation-related pathways. The early-stage lung adenocarcinoma (LUAD) prognostic model was constructed through the cancer genome atlas (TCGA) database, and three target genes (MMP12, NFE2, HOXC8) were screened to calculate the risk score of early-stage LUAD. The receiver operating characteristic (ROC) curve indicated that the model had good prognostic value, and the validation set (GSE50081, GSE11969 and GSE42127) from the gene expression omnibus (GEO) analysis indicated that the model had good stability, and the risk score was correlated with immune infiltrations to varying degrees. Multi-type survival analysis and immune infiltration analysis revealed that the transcriptome, methylation and the copy number variation (CNV) levels of the three genes were correlated with patient prognosis and some tumor microenvironment (TME) components. Drug sensitivity analysis found that the three genes may affect some anti-tumor drugs. The mRNA expression of immune checkpoint-related genes showed significant differences between the high and low group of the three genes, and there may be a mutual regulatory network between immune checkpoint-related genes and target genes. Tumor immune dysfunction and exclusion (TIDE) analysis found that three genes were associated with immunotherapy response and maybe the potential predictors to immunotherapy, consistent with the CTR-DB database analysis.ConclusionsFrom the perspective of data mining, this study suggests that MMP12, NFE2, and HOXC8 may be involved in tumor immune regulation and affect immunotherapy. They are expected to become markers of immunotherapy and are worthy of further experimental research.

Multi-objective optimal allocation of distributed generation considering the spatiotemporal correlation of wind-photovoltaic-load

• A multi-objective DG planning model is developed from the perspective of multi-dimensional data mining. • The model is used to solve the correlation analysis for wind speed, light intensity, and load demand of adjacent regions. • The spatiotemporal correlation, economic and system safety and environmental benefits are considered. • The effects of the seasonal difference on the favorable level of the DG's grid connection have been investigated. Aiming at the problem of the distributed generation (DG) planning caused by the strong spatiotemporal coupling between DG output and load demand in adjacent areas, a multi-objective planning model is proposed to describe the spatiotemporal correlation of sources. By combining the most weight supported tree (MWST) and depth first search (DFS), the method achieves the a priori requirement for constructing bayesian network (BN) structure using the K2 algorithm. Then, the MDK2-BN model is established through the measured data, which can describe the correlation between multi-dimensional wind-photovoltaic-load. A DG multi-objective programming model with maximum annual profit rate and minimum comprehensive operation risk is constructed. The results has three main advantages: (1) the MDK2-BN structure can achieve satisfactory results when dealing with small networks. (2) the MDK2-BN model conforms to the spatiotemporal correlation of the DG output, and the proposed configuration can improve the access capacity of DG. (3) the favorable level of the DG's grid connection can be effectively improved by considering the seasonal difference in performance and providing the planners with the decision-making references that balance the economic benefits, system operational safety, and environmental benefits.

A Data-Driven Smart Evaluation Framework for Teaching Effect Based on Fuzzy Comprehensive Analysis

In recent years, the epidemic of communicable diseases has boosted the prevalence of online teaching activities. But how to make smart evaluation towards teaching effect has always been a technical barrier. As consequence, this paper utilizes fuzzy comprehensive analysis to deal with this problem from the perspective of big data mining. In particular, it proposes a data-driven smart evaluation framework for teaching effect based on fuzzy comprehensive analysis. Firstly, business data is timely collected from online courses as the basis, including teacher performance, teaching contents, student feedback, etc. Specifically, the initial data is encoded into structured format, from which characteristics of students behaviors can be analyzed. Then, the fuzzy comprehensive analysis is utilized to calculate evaluation results of teaching effect. Some simulation experiments are conducted based on the computer programming design, in which the proposal technical framework is implemented on a developed Web platform. The experiments reflect that the proposal can well realize evaluation of teaching effect.

Investigating the Material Properties of Nodular Cast Iron from a Data Mining Perspective

Cast iron is a very common and useful metal alloy, characterized by its high carbon content (>4%) in the allotropic state of graphite. The correct shape and distribution of graphite are essential for ensuring that the material has the right properties. The present investigation examines the metallurgical and mechanical characterization of a spheroidal (nodular) cast iron, an alloy that derives its name and its excellent properties from the presence of graphite as spheroidal nodules. Experimental data are detected and considered from a data mining perspective, with the scope to extract new and little-known information. Specifically, a machine learning toolkit (i.e., Orange Data Mining) is used as a means of permitting supervised learners/classifiers (such as neural networks, k-nearest neighbors, and many others) to understand related metallurgical and mechanical features. An accuracy rate of over 90% can be considered as representative of the method. Finally, interesting considerations emerged regarding the dimensional effect on the variation in the solidification rates, microstructure, and properties.

Multivariate Statistical Analysis of Quality Improvement Effect of Innovation and Entrepreneurship Education Based on Random Matrix Theory

The reform can better improve students’ innovation and entrepreneurial skills and comprehensively improve the innovation and entrepreneurial competitiveness of college students, which is conducive to their survival and development in the market. Therefore, this paper analyzes the data generated in the educational process from the perspective of educational data mining and studies students’ entrepreneurial interests and motivations so as to improve the quality of students’ entrepreneurial guidance. The knowledge map, an important visualization tool in personalized learning systems, is used as a research object to analyze students’ online evaluation data. We develop a prototype system for improving teaching quality—a knowledge map generation and knowledge point recommendation path analysis system. We hope to further explore new ways and methods to promote the reform of innovation and entrepreneurship education in colleges and universities, giving full play to the proper role and value of innovation and entrepreneurship education.

Data mining in predictive maintenance systems: A taxonomy and systematic review

AbstractPredictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored system and minimizing the number of resources used in maintenance. Predictive maintenance is currently undergoing a revolution thanks to advances in industrial systems monitoring within the Industry 4.0 paradigm. Likewise, advances in artificial intelligence and data mining allow the processing of a great amount of data to provide more accurate and advanced predictive models. In this context, many actors have become interested in predictive maintenance research, becoming one of the most active areas of research in computing, where academia and industry converge. The objective of this paper is to conduct a systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective. The review presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem, relating the predictive maintenance tasks with the main data mining tasks to solve them. Finally, the paper presents significant challenges and future research directions in terms of the potential of data mining applied to predictive maintenance.This article is categorized under: Application Areas > Industry Specific Applications Technologies > Internet of Things

The Numeric Characteristics of Chinese A-Share Market Index Volatility, Model Simulation, and Forecasting

This study investigates the basic numeric characteristics of Chinese A-share market index volatility (i.e., the clustering, heteroscedasticity, and jumps) from the perspective of data mining. It presents a theoretical-empirical model based on these three major characteristics and conducts a maximum likelihood estimation to study Chinese A-share market return data empirically. Results show that, in full-sample or special periods, this model calibrates the A-share index volatility well and simulates in-sample volatility better than the four major empirical models adopted to study volatility. In out-of-sample forecasts, this model performs better than the other four models on the value-at-risk dates, which are the volatile days. This model can also decompose and explain the volatility of the Chinese A-share index. On the basis of GARCH, this study revises the volatility model proposed by Maheu and extends Engle’s research framework. Thus, this model is of theoretical significance. This model’s simulation and forecast functioning can contribute to regulatory expectation management and investment portfolio construction.