Accurate Mining Research Articles

An Advanced Apriori Algorithm Technology to Enhance Sports Data Mining

This paper explores the optimization of association rule mining in the context of physical education (PE) data, focusing on modifying the Apriori algorithm to enhance its efficiency and accuracy. Recognizing the unique characteristics of PE data—such as the consistent length and limited scope of physical test metrics—the study proposes a novel approach that combines transaction compression with hash technology to refine the Apriori algorithm. This enhanced algorithm aims to analyze the correlations between physical fitness indicators effectively and quickly to identify key factors influencing student performance. Experimental results demonstrate that the improved model significantly increases operational efficiency while maintaining high mining accuracy. Additionally, the research addresses the integration of ecological education within PE, emphasizing the dynamic balance concept of “balance-unbalance-adaptation-balance” in optimizing the educational ecosystem. The findings are helpful for providing practical tools for educators to advance this field.

Study on the accurate detection method of full‐polarimetric ground‐penetrating radar faults in mines based on modified Yamaguchi decomposition

AbstractObtaining information on tectonic tendencies is a prerequisite for intelligent and accurate mining in mines. In the special mine environment, the co‐polarized ground‐penetrating radar can only identify the spatial location of faults, and it is difficult to analyse the inclination information of fault structures. This paper proposes a mine full‐polarimetric ground‐penetrating radar fault tendency detection method based on this. First, based on the stacking characteristics of the coal depositional, this paper analyses the propagation law of the pulse electromagnetic wave in the coal seam and puts forward the assumption of the overlapping echo reflection of the fault structure. The reasonableness of the fault reflection assumption is verified through a numerical simulation study. Second, based on the cutting relationship of the fault to the coal seam, we divided the reflection structure of the fault structure into plane scattering and dihedral angle scattering. We realized the mingled echoes’ decomposition using the improved Yamaguchi decomposition technique. To analyse the applicability of the modified Yamaguchi and Freeman decomposition methods in the identification of fault inclination, we use the upright fault simulation data for the discussion, and we find that the improved Yamaguchi decomposition method is more advantageous in the identification of fault inclination in the mine. The decomposition results based on the simulation data of fault models with different dip angles found that when the dip angle of the fault is less than 90°, the scattering of the fault structure is dominated by planar scattering and dihedral angle scattering; when the dip angle of the fault is greater than or equal to 90°, the scattering of the fault structure is dominated by planar scattering, and the scattering power of the dihedral angle model is zero. By analysing the effect of fault strike on the decomposition results, it is found that the fault strike angle has little effect on the identification of fault tendency. Finally, the application potential of this paper's method is tested by constructing complex numerical models and probing experiments. Therefore, the method proposed in this paper can solve the fault tendency identification under a thick coal seam.

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

<p><span lang="AZ-LATIN">Tourist hotels (or tourist accommodations) are located near tourist attractions, primarily serving tourists. In recent years, with the gradual improvement of people’s living standards around the globe, tourists’ demands and standards for tourist hotel construction have been rising accordingly. In the context of technologization and informatization, various hotel booking platforms (Agoda, Booking, Trip, etc.) cover a large amount of review data in evaluating systems to reflect tourists’ demands. Meanwhile, identifying demand-oriented reviews and extracting core consumer demands from them is crucial for optimizing hotel services and enhancing tourist satisfaction. Therefore, this study explores the demands of tourists in tourist hotels from the perspective of text sentiment analysis and takes Macao, a famous tourist destination, as an example, based on reviews of tourist hotels on the Agoda site platform. Specifics are as follows: (1) Based on pointwise mutual information (PMI) and information entropy (IE), it realizes the identification of sentiment words in the field of tourist hotels and constructs a sentiment dictionary to address the problem of poor relevance between word segmentation results; (2) It summarizes the five types of reviews containing tourist demands (positive, negative, suggestion, demand, and comparison) and their characteristics to solve the ambiguity of texts and further accurately reveal the main demands of tourists; (3) It classifies tourist demands and group similar tourist demands into the same categories to address the problem of multiple expressions for the same demand. The present study provides empirical experiences from Macao’s hotels and contributes to the literature on text sentiment analysis in tourist hotels. Furthermore, the study results could enhance the mining accuracy and provide a detailed summarization of consumer demands and directions for the sustainable optimization improvement of tourist services.</span></p>

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

<p><span lang="AZ-LATIN">Tourist hotels (or tourist accommodations) are located near tourist attractions, primarily serving tourists. In recent years, with the gradual improvement of people’s living standards around the globe, tourists’ demands and standards for tourist hotel construction have been rising accordingly. In the context of technologization and informatization, various hotel booking platforms (Agoda, Booking, Trip, etc.) cover a large amount of review data in evaluating systems to reflect tourists’ demands. Meanwhile, identifying demand-oriented reviews and extracting core consumer demands from them is crucial for optimizing hotel services and enhancing tourist satisfaction. Therefore, this study explores the demands of tourists in tourist hotels from the perspective of text sentiment analysis and takes Macao, a famous tourist destination, as an example, based on reviews of tourist hotels on the Agoda site platform. Specifics are as follows: (1) Based on pointwise mutual information (PMI) and information entropy (IE), it realizes the identification of sentiment words in the field of tourist hotels and constructs a sentiment dictionary to address the problem of poor relevance between word segmentation results; (2) It summarizes the five types of reviews containing tourist demands (positive, negative, suggestion, demand, and comparison) and their characteristics to solve the ambiguity of texts and further accurately reveal the main demands of tourists; (3) It classifies tourist demands and group similar tourist demands into the same categories to address the problem of multiple expressions for the same demand. The present study provides empirical experiences from Macao’s hotels and contributes to the literature on text sentiment analysis in tourist hotels. Furthermore, the study results could enhance the mining accuracy and provide a detailed summarization of consumer demands and directions for the sustainable optimization improvement of tourist services.</span></p>

Graph Convolutional Spectral Clustering for Electricity Market Data Clustering

As the power grid undergoes transformation and the Internet’s influence grows, the electricity market is evolving towards informatization. The expanding scale of the power grid and the increasing complexity of operating conditions have generated a substantial amount of data in the power market. The traditional power marketing model is no longer suitable for the modern power market’s development trend. To tackle this challenge, this study employs random forest and RBF models for processing electricity market data. Additionally, it explores the synergy of graph convolutional network and spectral clustering algorithms to enhance the accuracy and efficiency of data mining, enabling a comprehensive analysis of data features. The experimental results successfully extracted various electricity consumption features. This approach contributes to the informatization efforts of power grid enterprises, enhances power data perception capabilities, and offers reliable support for decision makers.

DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the effect of chromatin interactions on target gene expression, thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements. In this study, we developed a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCCs) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and both proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences. Some important motifs were identified through DeepCBA; they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity. Importantly, experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression. Moreover, promoter editing and verification of two reported genes (ZmCLE7 and ZmVTE4) demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.

Deep Siamese Network for annual change detection in Beijing using Landsat satellite data

The detection of spatiotemporal changes in land use/land cover (LULC-SC) plays a paramount role in the analysis of smart cities, it can describe complex urban distribution, functions, and patterns. Chinas capital city has been developing rapidly in the past two decades, however, there are only few long-term studies on an annual scale of LULC-SC. To fill this research gap, we propose a remote sensing parallel framework for the detection of LULC-SC based on the combination of the Deep Siamese Network and long time series, which focuses on the spatial semantic information at the object level. A Landsat time series from 2002 to 2022 serves as input satellite data. First, we focus on building graph constructions at the object level and then use an autonomously constructed deeper-feature graph convolutional network (DF-GCN) to mine deeper features, spatial semantic, and relationships at the object level. Finally, the Siamese Network recognizes the changes in the spatial semantic tensors of long time series of Landsat images and quickly maps LULC-SC. The results prove that the proposed spatiotemporal change detection framework is effective in LULC-SC in Beijing. Compared with other networks, the optimal accuracy of semantic mining based on DF-GCN can reach about 90%. Over the past two decades, the LULC-SC of Beijing has changed in a complex way, with urbanization occurring primarily through the replacement of farmland. Consequently, the proposed framework can generate accurate LULC change maps at high temporal frequencies, which can contribute to a better comprehension of sustainable urban development and planning.

An Exploration of the Computer Big Data Mining Service Model Under Resource Sharing

In order to meet the diverse needs of users for data mining services and improve resource utilization and enterprise competitiveness, this article aims to construct a Big Data Analytics (BDA) data mining service model based on resource sharing mechanisms. This article designs a customized data mining service model for BDA based on its characteristics. In this model, the authors apply the improved Apriori algorithm to determine the optimization plan and improve the ant colony optimization algorithm to improve the efficiency and accuracy of data mining. By analyzing the experimental results, the scientificity and rationality of the proposed data mining service model for BDA were demonstrated, and the implementation strategy of the data mining model was improved. These research findings provide important references for BDA's data mining service model based on response surface modeling and also provide guidance for enterprises on how to better utilize resources and improve competitiveness when facing big data.

A method of data analysis based on division-mining-fusion strategy

With the advancement of data technology and storage services, the scale and complexity of data are rapidly growing. Consequently, promptly analyzing data and deriving precise insights have become urgent. Nevertheless, traditional methods struggle to balance the speed and accuracy of data mining. This paper proposes a data analysis technique called the Division-Mining-Fusion (DMF) strategy to tackle this challenge. Specifically, we divide a large-scale and complex dataset into multiple small-scale and simple sub-datasets. Then, we extract the knowledge embedded within each sub-dataset. Finally, we combine the extracted knowledge from each sub-dataset to accomplish learning tasks. To demonstrate the superior performance of the DMF strategy, we apply it to two fields: rough set theory and feature selection. The DMF strategy can accelerate the speed of data mining, enhance the accuracy of data analysis, and reduce the dimensionality of data. These advantages suggest that the DMF strategy outperforms traditional methods in processing data more efficiently. In addition, the number of sub-datasets is a crucial parameter of the DMF strategy. As the number of sub-datasets increases, the ability of the DMF strategy to analyze data continuously improves.

Research on the Optimization of Apriori Algorithm Based On Cloud Computing and Medical Information Data

This study proposes an Hadoop-based improved Apriori algorithm (IM Apriori) that optimizes the efficiency of data processing by reducing the set of candidate items and the time to calculate support. The improved algorithm solves two major bottlenecks in traditional Apriori algorithms: scanning databases frequently and generating a large number of candidate sets. Specific improvements include adopting new database mapping methods, avoiding repeatedly scanning the database, further pruning frequent and candidate sets, and calculating support using overlapping strategies to achieve efficient data processing. To verify the performance of the improved algorithm, simulation experiments were conducted on the Hadoop platform and compared with the traditional Apriori algorithm, BITXOR algorithm and several other association analysis algorithms. The experimental results show that the improved IM Apriori algorithm is better than the traditional and other improved algorithms in terms of efficiency and performance, which significantly reduces the number of database scans and alleviates the input / output bottleneck problem. Through this study, we prove the application potential of cloud computing based and improved data mining algorithm in medical data analysis, and improve the efficiency and accuracy of medical data mining.

Large-scale Data Mining Method based on Clustering Algorithm Combined with MAPREDUCE

With the continuous deepening and development of information technology, the diversity and amount of information in data continue to grow. Effectively mining these text data to extract valuable content has become an urgent task in the field of data research. This study combines the MapReduce distributed system with the K-means clustering algorithm to meet the challenges of large-scale data mining. At the same time, the paper use a distributed caching mechanism to solve the problem of repeated application of resources for multiple MapReduce collaborative operations and improve data mining efficiency. The combination of MapReduce's distributed computing and the advantages of K-means clustering algorithm provides an efficient and scalable method for large-scale data mining. Experimental results combining internal and external indicators show that the advantage of combining K-means with MapReduce is to fully utilize the distributed and parallel computing characteristics of MapReduce, providing users with an efficient and scalable data mining tool. Through this research, the paper provide new methods and insights for large-scale data mining, improving the efficiency and accuracy of data mining.

Inversion study on elastic–plastic material parameters of red sandstone in uniaxial compression test

In order to obtain the real material parameters of heterogeneous rock, the material parameters of red sandstone specimens under uniaxial compression tests are inverted based on the Digital Image Correlation (DIC) method and the Finite Element Model Updating (FEMU) method. The DIC method is employed to calculate the displacement field of red sandstone specimens during uniaxial compression loading. Concurrently, a uniaxial compression elastic–plastic finite element numerical model with non-uniform material parameters is developed based on the FEMU method. The model adopts the Mohr–Coulomb yield criterion and adjusts the boundary conditions in real-time to maintain consistency with the test. The vertical displacement field of the numerical model is juxtaposed with that of the test to construct the objective function. Optimization is achieved using the Artificial Fish Swarm algorithm, which enables the acquisition of the non-uniform distribution and evolution process of the material parameters of specimens at different loading moments. The results indicate that this method can spatially obtain the non-uniform distribution field of material parameters and temporally track the evolution of material parameters during the loading process. This research lays a solid foundation for enhancing the accuracy of intelligent coal mining and dynamic disaster monitoring and early warning in coal mines.

An empirical study of the impact of biological information dissemination in social media on public science literacy

Abstract In this paper, we first establish a locally converged bioinformatics dataset based on gradient sampling and design an optimal data mining control model to improve the accuracy of bioinformatics big data feature mining. The performance of the Compressive Tracking algorithm and Online Bosting algorithm is compared with the mining error as a test index. At the same time, we propose a social media information dissemination algorithm applicable to large-scale social network datasets, taking the degree value of each node as the node’s full influence and comparing and analyzing the dissemination influence of BP-IM, RAND and MC-CELF algorithms. Finally, taking public health big data as the research object, the least squares regression method was used to analyze the influence of the amount of public attention to bioinformatics scientific knowledge on their scientific literacy in different media. The results showed that there was a significant positive correlation between scientific literacy and willingness to engage in science participation behavior on social media when the amount of public attention to scientific information was β =0225, p <0.01. When more people are interested in scientific knowledge of bioinformatics on social media, their scientific literacy will improve.

Data Mining Analysis for Improving Decision-Making in Computer Management Information Systems

In this paper a decision tree-based data mining procedure for information systems is proposed to enhance the accuracy and efficiency of data mining. An enhanced C4.5 decision tree method based on cosine similarity is suggested to evaluate the information gain rate of characteristics and the information entropy of their values. When the information entropy variance among any two values for attributes is within the threshold range, the cosine similarity of the merging attribute values is determined, and the information gain rate of the attributes is recalculated. Large-scale data sets that conventional data processing methods are unable to handle successfully have given rise to the area of data mining. The prime objective is to look into how data mining technology is used in computer management information systems. The benefits of data mining technologies in computer management information systems are examined from a variety of angles in this study. In order to analyze and comprehend huge data sets and to derive knowledge that can be utilized to enhance the decision-making process in computer management information systems, the suggested solution makes use of a number of data mining techniques, including Clustering, Classification, and Association Rule Mining. The experimental analysis indicates that the time required by the proposed method to construct a decision tree is less than the time required by the GBDT, P-GBDT method and the C5.0 decision tree Hyperion image forest type fine classification method. The minimum time is not more than 15 seconds when compared with the minimum time saving of the other two methods. The time required by the C5.0 decision tree Hyperion image forest type fine classification method is always the greatest in comparison with the minimum time saving of the C5.0 decision tree. The classification accuracy of the proposed method for various datasets exceeds 95 percent, and the data mining efficacy is high. This method enhances the precision and efficacy of data mining in order to uncover valuable information concealed behind a large volume of data and maximize its value.

Privately vertically mining of sequential patterns based on differential privacy with high efficiency and utility

Sequential pattern mining is one of the fundamental tools for many important data analysis tasks, such as web browsing behavior analysis. Based on frequent patterns, decision-makers can obtain both economic gains and social values. Sequential data, on the other hand, frequently contain sensitive information, and directly analyzing these data will raise user concerns from a privacy perspective. Differential privacy (DP), as the most popular privacy model, has been employed to address this privacy concern. Most existing DP-Solutions are designed to combine horizontal sequence pattern mining algorithms with differential privacy. Due to the inefficiency of horizontal algorithms, their DP-Solutions cannot ensure high efficiency and accuracy while offering a high privacy guarantee. Therefore, we proposed privVertical, a new private sequence pattern mining scheme combining the vertical mining algorithm with differential privacy to achieve the above objective. Unlike DP-solutions based on horizontal algorithms, privVertical can promote efficiency by avoiding performing costly database scans or costly projection database constructions. Moreover, to promote accuracy, a differentially private hash MapList (called privHashMap) is designed to record frequent concurrency items and their noisy support based on the Sparse Vector Technique. PrivHashMap is used to pre-pruning excessive infrequent candidate sequences in private mining, and Sparse Vector Technique is used to promote the accuracy of PrivHashMap. After pruning these invalid candidate sequences, less noise is required to achieve the same level of privacy, increasing the accuracy of private mining. Theoretical privacy analysis proves privVertical satisfies varepsilon-differential privacy. Experiments show that privVertical achieves higher accuracy and efficiency while achieving the same privacy level.

Scene clusters, causes, spatial patterns and strategies in the cultural landscape heritage of Tang Poetry Road in Eastern Zhejiang based on text mining

The burgeoning field of digital humanities has provided important modern technological means for text mining in literary works. Chinese classical poetry, as a treasure in the world’s artistic treasury, holds significant value in recognizing the heritage of world culture. In this study, taking the 1589 Tang poems from the Tang Poetry Road in Eastern Zhejiang as an example, we constructed a research framework that explores the aesthetics of classical Chinese poetry landscapes and spatial imagery at the urban agglomeration scale by utilizing geographic and analytical tools such as Python programming, Gephi co-occurrence semantic networks, and GIS kernel density analysis. The framework exhibits three key innovations: (1) a text processing approach that treats individual characters as semantic units in ancient poetry texts, (2) a combined approach of Python programming techniques and Gephi visualization tool for social network analysis, and (3) a study focusing on the integration of textual and spatial aspects of literary landscape heritage corridors at the urban cluster scale. The constructed framework greatly enhances the efficiency and accuracy of Tang poetry text mining, it enables the extraction of natural and cultural landscape spatial imagery along the Tang Poetry Road, the construction of scene depictions, the identification of key regions within the scenes, and the derivation of location-specific strategies. This study broadens the scope of exploring the cultural heritage value of Tang poetry literature and provides practical guidance for the development of cross-regional heritage corridors.

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.

Causal discovery approach with reinforcement learning for risk factors of type II diabetes mellitus

BackgroundStatistical correlation analysis is currently the most typically used approach for investigating the risk factors of type 2 diabetes mellitus (T2DM). However, this approach does not readily reveal the causal relationships between risk factors and rarely describes the causal relationships visually.ResultsConsidering the superiority of reinforcement learning in prediction, a causal discovery approach with reinforcement learning for T2DM risk factors is proposed herein. First, a reinforcement learning model is constructed for T2DM risk factors. Second, the process involved in the causal discovery method for T2DM risk factors is detailed. Finally, several experiments are designed based on diabetes datasets and used to verify the proposed approach.ConclusionsThe experimental results show that the proposed approach improves the accuracy of causality mining between T2DM risk factors and provides new evidence to researchers engaged in T2DM prevention and treatment research.

Edge-weights-based method to identify influential spreaders in complex networks

Identifying influential nodes has drawn great attention in recent years. In this paper, a novel method for identifying influential spreaders based on potential edge weights (WDK for simplicity) for both undirected and unweighted networks is proposed. Degree and k-shell of a node and its neighbors are considered simultaneously, which are regarded as the weight of the edge directly connected to the node. The algorithm considers not only the local information of nodes but also their location information. The proposed method not only improves the accuracy of node mining but also has approximately linear time complexity, which indicates that the proposed method is suitable for large-scale networks. In order to validate the effectiveness of the proposed method, different evaluation indexes are introduced in nine real networks. Compared with five classical key nodes identification methods, the experimental results show that the proposed method performs optimally in all networks.

Data analysis with performance and privacy enhanced classification

Abstract Privacy is the main concern in cyberspace because, every single click of a user on Internet is recognized and analyzed for different purposes like credit card purchase records, healthcare records, business, personalized shopping store experience to the user, deciding marketing strategy, and the list goes on. Here, the user’s personal information is considered a risk process. Though data mining applications focus on statistically useful patterns and not on the personal data of individuals, there is a threat of unrestricted access to individual records. Also, it is necessary to maintain the secrecy of data while retaining the accuracy of data classification and quality as well. For real-time applications, the data analytics carried out should be time efficient. Here, the proposed Convolution-based Privacy Preserving Algorithm (C-PPA) transforms the input into lower dimensions while preserving privacy which leads to better mining accuracy. The proposed algorithm is evaluated over different privacy-preserving metrics like accuracy, precision, recall, and F1-measure. Simulations carried out show that the average increment in the accuracy of C-PPA is 14.15 for Convolutional Neural Network (CNN) classifier when compared with results without C-PPA. Overlap-add C-PPA is proposed for parallel processing which is based on overlap-add convolution. It shows an average accuracy increment of 12.49 for CNN. The analytics show that the algorithm benefits regarding privacy preservation, data utility, and performance. Since the algorithm works on lowering the dimensions of data, the communication cost over the Internet is also reduced.