Issue In Data Mining Research Articles

An entity alignment method with attribute augmentation and contrastive learning

Abstract The entity alignment(EA) task is to identify entities with the same semantics in the knowledge graph(KG), an essential issue in KG fusion and big data mining. Existing entity alignment methods mainly adopt graph embedding-based methods. However, they still have some shortcomings. First, they heavily rely on high-quality alignment seed and external semantic information. Secondly, the present attention mechanism focuses on the entire graph information, neglecting the noise of attribute information. This paper proposes an EA approach based on Attribute Augmentation and Contrastive Learning (AACL). Our method introduces attribute augmentation to enhance the structure information of knowledge graphs and reduce dependence on alignment seed. A masked attention mechanism is developed to emphasize important attribute information and mask out invalid attributes to better capture semantic dependencies in the KG. Experimental results on three public datasets indicate that our AACL outperforms the present entity alignment approaches.

Processing imbalanced medical data at the data level with assisted-reproduction data as an example

ObjectiveData imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification models. We focus on quantifying the effects of different imbalance degrees and sample sizes on model performance, identifying optimal cut-off values, and evaluating the efficacy of various methods to enhance model accuracy in highly imbalanced and small sample size scenarios.MethodsWe collected medical records of patients receiving assisted reproductive treatment in a reproductive medicine center. Random forest was used to screen the key variables for the prediction target. Various datasets with different imbalance degrees and sample sizes were constructed to compare the classification performance of logistic regression models. Metrics such as AUC, G-mean, F1-Score, Accuracy, Recall, and Precision were used for evaluation. Four imbalance treatment methods (SMOTE, ADASYN, OSS, and CNN) were applied to datasets with low positive rates and small sample sizes to assess their effectiveness.ResultsThe logistic model’s performance was low when the positive rate was below 10% but stabilized beyond this threshold. Similarly, sample sizes below 1200 yielded poor results, with improvement seen above this threshold. For robustness, the optimal cut-offs for positive rate and sample size were identified as 15% and 1500, respectively. SMOTE and ADASYN oversampling significantly improved classification performance in datasets with low positive rates and small sample sizes.ConclusionsThe study identifies a positive rate of 15% and a sample size of 1500 as optimal cut-offs for stable logistic model performance. For datasets with low positive rates and small sample sizes, SMOTE and ADASYN are recommended to improve balance and model accuracy.

Data Mining Techniques Against Cyber Threats: A Review

Abstract Data mining is a technique used to extract useful data from existing databases. Those datasets are now being shared globally. Secure communication and confidentiality are required because data from multiple sources must be collected and stored in one central location. Data mining technology involves methods that rapidly and efficiently convert vast quantities of data into relevant insights adapted to the user's requirements. Unfortunately, the utilization of data mining expertise to acquire sensitive personal information poses a threat to individuals' privacy rights. This paper provides a review of the current techniques for preventing cyber risks and safeguarding privacy through the application of data mining. Data mining is used to examine, analyze, and figure out the structure and behavior of data mining organizations. Implementing data mining with optimal outcomes is a challenging task. In the past decade, academics have extensively studied many elements of data extraction. Therefore, it is crucial to provide published research evidence pertaining to this field. For this study, a thorough evaluation was carried out of more than thirty research papers sourced from reputable literature databases. The objective was to extract significant information regarding the field of data mining. The collected data was then used to address various study inquiries about cutting-edge extraction methodologies, data mining mechanisms in cyber dangers, data extraction procedures, algorithms, and evaluation techniques. This paper discusses various research areas and issues in data mining, serving as a valuable reference for researchers in this domain.

Hybrid Aquila optimizer for efficient classification with probabilistic neural networks

The model of a probabilistic neural network (PNN) is commonly utilized for classification and pattern recognition issues in data mining. An approach frequently used to enhance its effectiveness is the adjustment of PNN classifier parameters through the outcomes of metaheuristic optimization strategies. Since PNN employs a limited set of instructions, metaheuristic algorithms provide an efficient way to modify its parameters. In this study, we have employed the Aquila optimizer algorithm (AO), a contemporary algorithm, to modify PNN parameters. We have proposed two methods: Aquila optimizer based probabilistic neural network (AO-PNN), which uses both local and global search capabilities of AO, and hybrid Aquila optimizer and simulated annealing based probabilistic neural network (AOS-PNN), which integrates the global search abilities of AO with the local search mechanism of simulated annealing (SA). Our experimental results indicate that both AO-PNN and AOS-PNN perform better than the PNN model in terms of accuracy across all datasets. This suggests that they have the potential to generate more precise results when utilized to improve PNN parameters. Moreover, our hybridization technique, AOS-PNN, is more effective than AO-PNN, as evidenced by classification experiments accuracy, data distribution, convergence speed, and significance. We have also compared our suggested approaches with three different methodologies, namely Coronavirus herd immunity optimizer based probabilistic neural network (CHIO-PNN), African buffalo algorithm based probabilistic neural network (ABO-PNN), and β-hill climbing. We have found that AO-PNN and AOS-PNN have achieved significantly higher classification accuracy rates of 90.68 and 93.95, respectively.

Digging Deeper With Machine Learning for Unbalanced Multimedia Data Categorization

Since many real-world data sets have skewed class distributions—in which the majority of data instances (examples) belong to one class and considerably fewer instances belong to others—classifying unbalanced data is an important area of research. While minority instances (fraud in banking operations, abnormal cells in medical data, etc.) in many applications actually represent the concept of interest, a classifier induced from an imbalanced data set is more likely to be biassed towards the majority class and show very poor classification accuracy for the minority class. Unbalanced data classification, particularly for multimedia data, continues to be one of the most difficult issues in data mining and machine learning, despite substantial research efforts. In this research, we present an extended deep learning strategy to address this difficulty and get encouraging results in the classification of skewed multimedia data sets. In particular, we examine the combination of advanced empirical research on convolutional neural networks (CNNs), a cutting-edge deep learning technique, and bootstrapping techniques. Given that deep learning techniques, like CNNs, are typically computationally costly, we suggest feeding low-level features to CNNs and demonstrate that this may be done in a way that saves a significant amount of training time while still producing promising results. The experimental findings demonstrate how well our methodology performs in the TRECVID data set when it comes to categorising highly unbalanced data.

Identification of stock market manipulation using a hybrid ensemble approach

Anomaly detection in time series data is a complex data mining issue with many useful, real-world applications. Anomalies in datasets represent deviations in the expected behaviour of a system and can indicate rare but significant events that require intervention. Market manipulation is a serious issue in financial jurisdictions worldwide, with financial regulators such as the SEC constantly trying to prevent it and prosecute those guilty of it. This paper makes use of state-of-the-art deep learning techniques as well as more classical statistical techniques in order to detect anomalies in five real-world datasets. The predictions of these models are then aggregated in two different ensemble models. The results of the individual models as well as the ensemble models, are evaluated, and F1-Score measures performance. Nine individual models, consisting of three models based on LSTM with Dynamic Thresholding, three ARIMA models and three Exponential Smoothing models, were used to generate predictions of anomalies based on daily trading volumes. The individual predictions of these models were then aggregated, with two different ensemble methods being used, namely the majority voting ensemble method and the ensemble averaging aggregation method. While both performed well, the majority voting ensemble method was seen to be the superior method in this study, with an average F1Score of 0.494, compared to an F1Score of 0.414 for the ensemble averaging aggregation method.

A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation

. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets.

Taobao transaction data mining based on time series evaluation under the background of big data

With the emergence of e-commerce, more and more people conduct transactions through the Internet, thus resulting in a large number of transaction data. Data mining is to decompose a large amount of data according to data rules, and analyze network transaction data, so as to provide necessary digital links for companies to analyze the market and develop business. Although time series data mining is smaller than other types of data mining, it is also an important issue of data mining. In the real world, the correlation between data and time is very common. The study of time series model plays a very important role in data mining. Due to different purposes, Taobao data analysis is also different. In addition to statistics, at present, the in-depth research and analysis of Taobao data are relatively insufficient, and the analysis of Taobao transaction data based on time series is rare. In order to improve the accuracy of Taobao transaction data mining and better formulate Taobao marketing strategy, this paper used time series data mining technology to mine Taobao transaction data. This paper first introduced the role of Taobao transaction data mining, and then described the calculation method of time series data mining, including the re-description of time series and the similarity measurement of time series. Finally, through a series of processes such as data collection, data processing and data feature extraction, the data mining model for Taobao transaction was established, and two data prediction evaluation indicators, namely prediction accuracy and entropy, were proposed. The experimental part verified the effect of Taobao transaction data mining. The experimental results showed that the data mining model moment had good data prediction accuracy and entropy. The average data prediction accuracy was 94.26%, and the data mining ability was strong.

Knowledge acquisition of multi-granularity ordered information systems

Knowledge reduction is an essential issue in data mining. However, most of the existing studies consider the problem of knowledge reduction in a single-scale system. When the system expands to a multi-scale information system (MIS), the existing methods are not available. To overcome this challenge, we design a universal framework to reduce attributes and select optimal scales (OSs) based on the dominance relation in ordered information systems (OISs). Firstly, we analyze the effect of scale on rough approximations and correlation measures in multi-scale ordered information systems (MOISs). Secondly, the relation between attribute reduction and OS selection is explored in OISs. Moreover, the corresponding algorithms are designed around both tasks. Due to our universal framework considering the decisions at the finest, optimal and coarsest scale, our study is an extension of the three-way decision. Thirdly, a novel MOIS construction algorithm is explored to transform an OIS into an MIS. Thus, the proposed reduction algorithms can be operated in any OIS. Experiments substantiate that models trained by data at the OS can improve their stability and the attribute reduction task is also suitable for the proposed algorithm.

Early Prediction of Students’ Performance Using a Deep Neural Network Based on Online Learning Activity Sequence

Predicting students’ performance is one of the most important issues in educational data mining. In this study, a method for representing students’ partial sequence of learning activities is proposed, and an early prediction model of students’ performance is designed based on a deep neural network. This model uses a pre-trained autoencoder to extract latent features from the sequence in order to make predictions. The experimental results show that: (1) compared with demographic features and assessment scores, 20% and wholly online learning activity sequences can achieve a classifier accuracy of 0.5 and 0.84, respectively, which can be used for an early prediction of students’ performance; (2) the proposed autoencoder can extract latent features from the original sequence effectively, and the accuracy of the prediction can be improved more than 30% by using latent features; (3) after using distance-based oversampling on the imbalanced training datasets, the end-to-end prediction model achieves an accuracy of more than 80% and has a better performance for non-major academic grades.

UNSW‐NB15 computer security dataset: Analysis through visualization

AbstractClass imbalance refers to a major issue in data mining where data with unequal class distribution can deteriorate classification performance. Although it alone affects the performance of the classifiers, the joint‐effect of class imbalance and overlap is more damaging. Data overlap happens when multiple classes are assigned to a single data point causing the classifiers to misidentify the class boundaries. This study offers a deep insight into the intricacies of the UNSW‐NB15 dataset and two issues that may lead the data‐driven models to demonstrate poor performance. The most commonly used visualization methods such as bar chart, 3D and 2D scatter plots, intercluster distance map, and parallel coordinate diagram were employed to depict the data imbalanced and overlap. However, their limitations in capturing the overlapping issue led us to propose an accurate, easy‐to‐interpret, and scalable overlapping visualization method. The method clearly detects the data overlap and illustrates the effect of several data scalers in dealing with the data overlap. To verify the accuracy of the proposed method, a number of classifiers were implemented along with the scalers and the calculated AUC scores were compared to those calculated from the classifiers that were implemented on the original dataset.

A Cluster-based Undersampling Technique for Multiclass Skewed Datasets

Imbalanced data classification is a demanding issue in data mining and machine learning. Models that learn with imbalanced input generate feeble performance in the minority class. Resampling methods can handle this issue and balance the skewed dataset. Cluster-based Undersampling (CUS) and Near-Miss (NM) techniques are widely used in imbalanced learning. However, these methods suffer from some serious flaws. CUS averts the impact of the distance factor on instances over the majority class. Near-miss method discards the inter-class data within the majority of class elements. To overcome these flaws, this study has come up with an undersampling technique called Adaptive K-means Clustering Undersampling (AKCUS). The proposed technique blends the distance factor and clustering over the majority class. The performance of the proposed method was analyzed with the aid of an experimental study. Three multiminority datasets with different imbalance ratios were selected and the models were created using K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF) classifiers. The experimental results show that AKCUS can attain better efficacy than the benchmark methods over multiminority datasets with high imbalance ratios.

A Review of Multi-Label Feature Selection

Feature selection has become a vital issue in data mining and machine learning. But some challenges have been outstanding when trying to improve the performance of feature selection, such as small sample, uncertain classes, complex features, complementation and redundancy between each feature. In this paper, firstly the background of feature selection is introduced. Then we have presented a new perspective to analyze multi-label feature selection and provided typical papers on different classifications. To further analyze these algorithms, evaluation criterion on results of multi-label feature selection is summarized. Finally, some reflects on research directions, future works and conclusions are organized.

Data Mining (Concepts, Algorithms) and Its Application to predict and Control Covid-19 Epidemic

In recent years, the issue of data mining has been considered by researchers due to the widespread access to large amount of data, the urgent need for information, proper, fast and accurate identification and diagnosis of various topics. Data mining discovers valid, fresh and useful patterns of available data which can provide valuable analytics for very large data sets. Today, the scope of its use has expanded and various data mining algorithms are used to identify and predict important issues in the field of health. The health sector is in the greatest need for data mining. The move from traditional medicine to evidence-based medicine is one of the things that can confirm this. In today's world, Covid-19 quickly hit other countries in addition to China like a hurricane, killing many people around the world. Due to the large amount of data in the field of medicine, the data mining process has played a very effective role in the management of various diseases such as prognosis, diagnosis and treatment. The main purpose of using data mining algorithms in medical sciences is to make better use of databases and discover hidden knowledge in order to help physicians make better decisions. The present article, which is prepared in a review manner using internet and library resources, tries to examine the concepts, data mining algorithms and their application in the field of health, especially to help diagnosing Covid-19. Finally, practical suggestions are provided.

A state‐of‐the‐art survey of predicting students' performance using artificial neural networks

AbstractPredicting students' performance is one of the most important issue in educational data mining. In order to investigate the state‐of‐the‐art research development in predicting students' performance by using artificial neural networks (ANN), we conducted a survey on 39 important studies on this issue from 2016 to 2021. The results show that: (1) objectives of most prediction model is the performance of learners on the program and course; (2) datasets used for training prediction model are collected from logs of the learning management system; (3) the most commonly used ANN is feedforward neural network; (4) researchers use stochastic gradient descent and Adam algorithm to optimizes the parameters in ANN and configure hyper parameters of ANN manually; (5) feature selection is not necessary because ANN can automatically adjust the weights of artificial neurons; and (6) ANN has better performance than the classical classifiers in predicting student performance. The challenges in previous studies were comprehensively analyzed, suggestions for future research were put forward.

A Literature Review on Various Disease using Data Mining and Machine Learning Techniques

A huge number of data mining tools have been developed over the decades, due to widespread and complexity involved in building the data mining applications. Many tools use various algorithm base and methods to carry out many data mining tasks. Every tool has its own pros and cons.Data mining gives an efficient way to retrieve the required data or information from raw and heterogeneous data. There are many methods to predict the medical disease with high accuracy and classification. There are many challenges and issues in data mining for applicable in medical practice. To extract the information and minimize the effort of an expert system for other clinical concern like drug addiction. This review paper aimed at evaluating and analysing a range of data mining tools and techniques for optimally predicting the numerous medical diseases to provide the healthcare section with high competence and more effectiveness. Data mining gives an efficient way to retrieve the required data or information from raw and heterogeneous data. There are many methods to predict the medical disease with high accuracy and classification. There are many challenges and issues in data mining for applicable in medical practice. To extract the information and minimize the effort of an expert system for other clinical concern like drug addiction.

SGAE: Stacked Graph Autoencoder for Deep Clustering

Unsupervised clustering is a crucial issue in data mining and pattern recognition. Based on deep learning paradigms, deep clustering algorithms have been studied extensively and obtained superior performance in various applications. However, most of previous methods did not use helpful information from neighborhood relations to form group-separated space, and the feature embedding is usually distorted during the training process. To tackle the former limitation, we develop a graph convolution based unsupervised learning algorithm named Stacked Graph Autoencoder (SGAE). Specifically, SGAE utilizes the message passing mechanism to aggregate information from neighbors and obtain a meaningful and separated latent representation. Since the adjacency matrix is unavailable in clustering tasks, a graph construction approach with two pruning strategies is introduced to generate a transition matrix. To reduce the distortion caused by the multi-layered network training process, we further propose a topological structure preservation mechanism. It uses the constructed adjacency graph as supervised information, to maintain the relationship between nodes in the original space. Experiments on several popular benchmark datasets show that SGAE achieves significant improvements compared to unsupervised and semi-supervised deep clustering methods.

Sampling strategies for handling data imbalance problem: An Extensive Review

The imbalanced data classification is a major issue in data mining. Many researchers have proposed various solutions which addressed imbalanced data problem which is broadly categorized into data level and algorithm level. Class distributions are adjusted in data level method. Creating an algorithm or modifying the existing algorithm is an appropriate approach used in algorithm level method. Imbalanced data classification problem can be resolved by means of Sampling, Random over sampling, Random under sampling, Resampling and by SMOTE (Synthetic Minority Oversampling Techniques). Resampling includes k-means clustering, density-based clustering, neural networks and ensemble. However, no algorithm or a method has an ability to remove bias in data classification, thereby integration of kernel methods with sampling methods or integration of sampling and boosting methods or integration Kernel based with Support Vector Machines (SVM) need to be performed a great extent to get the desired accuracy and performance. The main objective of this paper is to focus on various sampling strategies that are based on sampling and resampling methods and improving the concept of learning within class imbalanced data. It also explains the objectives of the models used by several researchers and emphasized the performance along with the outcomes.

Fuzzy support vector machine for classification of time series data: A simulation study

Support vector machine (SVM) has become one of most developed methods for classification, focusing on cross-sectional analysis. However, classification of time series data is an important issue in statistics and data mining. Classification of time series data using SVMs that focus on cross-sectional data leads to improper classification, and hence, the SVM needs to be extended for handling time series dataset. As with cross-section data, the problem of imbalanced data is also common in time series data. Fuzzy method has been proven to be capable of overcoming the case of imbalanced data. In this paper, we developed a Fuzzy Support Vector Machine (FSVM) model to classify time series data with imbalanced class. The proposed method puts the fuzzy membership function on the constraint function. Through simulation studies, this research aims to assess the performance of the developed FSVM in classifying time series data. Based on the classification accuracy criteria, we prove that the proposed FSVM method outperforms the standard SVM method for the classification of multiclass time series data.

Distributed Clustering Approach by Apache Pyspark Based on SEER for Clinical Data

Data clustering is a thoroughly studied data mining issue. As the amount of information being analyzed grows exponentially, there are several problems with clustering diagnostic large datasets like the monitoring, microbiology, and end results (SEER) carcinoma feature sets. These traditional clustering methods are severely constrained in terms of speed, productivity, and adaptability. This paper summarizes the most modern distributed clustering algorithms, organized according to the computing platforms used to process vast volumes of data. The purpose of this work was to offer an optimized distributed clustering strategy for reducing the algorithm’s total execution time. We obtained, preprocessed, and analyzed clinical SEER data on liver cancer, respiratory cancer, human immunodeficiency virus (HIV)-related lymphoma, and lung cancer for large-scale data clustering analysis. Three major contributions and their effects were covered in this paper: To begin, three current Pyspark distributed clustering algorithms were evaluated on SEER clinical data using a simulated New York cancer dataset. Second, systemic inflammatory response syndrome (SIRS) model inference was done and described using three SEER cancer datasets. Third, employing lung cancer data, we suggested an optimized distributed bisecting [Formula: see text]-means method. We have shown the outcomes of our suggested optimized distributed clustering technique, demonstrating the performance enhancement.