Data Mining Applications Research Articles

Proactive berth scheduling with data‐driven buffer time in container terminals

AbstractGlobal shipment volumes have been increasing due to changes in the business environment of e‐commerce and manufacturing. Consequently, container vessels carry more cargo for international trade, increasing uncertainties in terminal management. Terminal operators manage terminals by establishing a proactive schedule that responds to disruptions such as vessel delays, and the introduction of buffer time is a representative proactive strategy. In this study, by analyzing historical delay data with machine learning, we propose data‐driven buffer times to consider the heterogeneous arrival uncertainty of vessels. Thus, we proactive scheduling with data‐driven buffer times according to the desired robustness levels. This is a novel study on berth scheduling that applies data mining approaches to improve operations research techniques. Numerical experiments were conducted on the berth scheduling with time‐invariant quay crane assignment using real‐life data to validate the effectiveness of the proposed method. These experimental results revealed that applying the data‐driven buffer time could effectively reduce the cost incurred at the terminal by balancing baseline and recovery costs. In addition, our proposed methodology ensured the quality of the solution compared with a stochastic method and reduced the computational burden of a stochastic problem by using the data‐driven buffer times obtained prior to the solution construction. Therefore, the proposed method can be introduced into terminal operations to overcome the deficiencies of traditional approaches in terms of academic perspective.

Web-based Clustering Application for Determining and Understanding Student Engagement Levels in Virtual Learning Environments

The increasing use of virtual learning environments (VLEs) in recent years has transformed teaching and learning methods. Universities now combine VLEs with traditional classrooms to accommodate hybrid teaching and learning approaches. However, student engagement with VLEs varies, and universities lack the tools to effectively determine and analyse VLE engagement. Consequently, data-driven decision-making regarding VLE usage remains a challenge for universities. This study thus proposed a user-friendly web-based application, using a R shiny framework, to determine and understand student engagement levels in VLEs. In this study, two clustering methods, K-means and Gaussian Mixture Model (GMM) were compared, to identify the most effective method for the proposed application. The results indicated that GMM outperforms K-means by generating more accurate and comprehensive groupings of student engagement levels. One key advantage of the GMM method is its ability to capture uncertainty and provide probabilities of student membership in each level of engagement, which enhances its usefulness for decision-making. Furthermore, the GMM method achieves these outcomes efficiently, saving valuable learning time. This research holds significant implications for education by providing valuable guidance for the development of Educational Data Mining (EDM) applications. Universities can leverage these applications to gain deep insights into VLE usage and enhance their understanding of student engagement. By adopting this web-based application, educators and administrators can make informed decisions and tailor interventions to optimize student learning experiences within VLEs. Keywords: Virtual Learning Environments, Student Engagement, Clustering.

A Review Paper on Data Mining Methods and their Applications

This research paper provides a comprehensive review of data mining methods and their applications. Over the past decade, data mining has emerged as a crucial tool in extracting useful patterns and insights from large datasets across various domains. The paper begins by outlining the foundational principles and techniques of data mining, including classification, clustering, association rule mining, and anomaly detection. It then proceeds to explore the evolution and advancements of these techniques throughout the specified timeframe, highlighting key developments, methodologies, and challenges encountered in their application. Furthermore, the paper discusses notable applications of data mining in real-world scenarios such as healthcare, finance, telecommunications, and e-commerce. These applications demonstrate the versatility and effectiveness of data mining in solving complex problems, improving decision-making processes, and enhancing business intelligence. Moreover, the review addresses the impact of technological advancements and the proliferation of digital data on the field of data mining during the specified period. By synthesizing insights from existing literature and research studies, this review aims to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the landscape of data mining methods and their practical implications.

A Review Paper on Data Mining Methods and Their Applications

This research paper provides a comprehensive review of data mining methods and their applications. Over the past decade, data mining has emerged as a crucial tool in extracting useful patterns and insights from large datasets across various domains. The paper begins by outlining the foundational principles and techniques of data mining, including classification, clustering, association rule mining, and anomaly detection. It then proceeds to explore the evolution and advancements of these techniques throughout the specified timeframe, highlighting key developments, methodologies, and challenges encountered in their application. Furthermore, the paper discusses notable applications of data mining in real-world scenarios such as healthcare, finance, telecommunications, and e-commerce. These applications demonstrate the versatility and effectiveness of data mining in solving complex problems, improving decision-making processes, and enhancing business intelligence. Moreover, the review addresses the impact of technological advancements and the proliferation of digital data on the field of data mining during the specified period. By synthesizing insights from existing literature and research studies, this review aims to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the landscape of data mining methods and their practical implications.

Exploration of cystathionine β-synthase and cystathionine γ-lyase gene expression poor prognostic predictive value with the application of bioinformatics data mining in liver cancer

Objective: To study the relationship between cystathionine β-synthase (CBS) and cystathionine γ-lyase (CTH) genes-related signaling pathways in liver cancer cells. Methods: We conducted a correlation analysis between the clinical features of CBS and CTH gene expression by mining the GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) databases of liver cancer. Additionally, liver cancer cell lines were verified by immunoblotting. Results: CBS and CTH expressions were significantly lower in tumors than in non-tumors (P < 0.05). COX regression result showed that CBS was an independent risk factor for the poor prognosis of liver cancer cells (HR=0.65, P = 0.02). A univariate logistic regression analysis was performed on the different tumor stages focusing on the CBS gene, which showed that TNM stage II verses I (P = 0.01, OR=0.50), stage III verses I (P = 0.03, OR=0.56), T stage T2 verses T1 (P < 0.01, OR=0.43), and T3 stage verses T1 (P = 0.02, OR=0.54) were significantly lower in liver cancer. TNM stage III verses I (P = 0.01, OR=0.50), Edmondson stage II verses I (P = 0.03, OR=0.48), stage III verses stage I (P < 0.01, OR=0.30), stage IV verses I (P = 0.03, OR=0.22), and T stage T3 verses T1(P = 0.03, OR=0.22) of the CTH gene expressions were significantly lower in liver cancer. GSEA enrichment analysis result revealed that the signaling pathway most correlated with the expression of CTH and CBS genes in liver cancer cells was cytochrome P450 (CYP450) (FDR Q < 0.01, FWER P < 0.01). Western blot results showed that the expression of the CTH downstream protein CSE was reduced in HCC cell lines such as HLE and Hep3B cells compared with the human immortalized liver cell line HL-7702. Conclusion: CBS and CTH gene expressions are lower in tumor tissue than in normal tissue groups. The CBS gene is an independent risk factor for poor prognosis in stem cell carcinoma. The cytochrome P450 is the signaling pathway most closely related to the CBS and CTH genes.

Pengembangan Aplikasi Web Berbasis Algoritma C5.0 Memprediksi Prestasi Siswa Berdasarkan Gaya Belajar

&lt;span lang="IN"&gt;The application of data mining in education has recently been developed. It is used to predict many aspects of learning. This research aims at finding out how to develop a web-based application using algorithm C5.0 to predict students’ achievement based on their learning styles. This study was conducted in Madrasah Tsanawiyah Darul Ilmi Batangkuis, Kabupaten Deli Serdang, Sumatera Utara. This research uses a CRISP-DM method to develop a web-based application. The result of data analysis shows that data mining can be used to predict students learning achievement based on their learning styles, which are categorized as visual, auditory, and kinesthetic. Predictions on students’ learning achievement can be found by using this application. The accuracy level of this application gains 100% and the error deviation is 0%. The data manual calculation of this research shows that the learning style that can be developed is kinesthetic.&lt;/span&gt;

Disentangling the Computational Complexity of Network Untangling

We study the network untangling problem introduced by Rozenshtein et al. (Data Min. Knowl. Disc. 35(1), 213–247, 2021), which is a variant of Vertex Cover on temporal graphs–graphs whose edge set changes over discrete time steps. They introduce two problem variants. The goal is to select at most k time intervals for each vertex such that all time-edges are covered and (depending on the problem variant) either the maximum interval length or the total sum of interval lengths is minimized. This problem has data mining applications in finding activity timelines that explain the interactions of entities in complex networks. Both variants of the problem are NP-hard. In this paper, we initiate a multivariate complexity analysis involving the following parameters: number of vertices, lifetime of the temporal graph, number of intervals per vertex, and the interval length bound. For both problem versions, we (almost) completely settle the parameterized complexity for all combinations of those four parameters, thereby delineating the border of fixed-parameter tractability.

A fusion of centrality and correlation for feature selection

The rapid development of computer and database technologies has led to the high growth of large-scale datasets. This produces an important issue for data mining applications called the curse of dimensionality, where the number of features is much higher than the number of patterns. One of the dimensionality reduction approaches is feature selection, which can increase the accuracy of these applications and reduce their computational complexity. This paper proposes a novel feature selection method to reduce the dimensionality and computational complexity in high-dimensional data processing. First, based on the centrality and Fisher score, a probabilistic strategy metric is proposed to measure the influence of features. Second, a new discriminant function is proposed to determine whether one feature should be selected. It can automatically calculate weight parameters to balance the relevance of a feature to class labels and the redundancy of the selected feature subset. Finally, a new method is proposed by combining the maximum information coefficient (MIC), total information (TI) and centrality technique, named MTC_FS. The experimental results show that the average accuracy of the MTC_FS improves by 1.8% compared with the best baseline, and the comprehensive performance of the MTC_FS is superior to all baselines on 12 public datasets. MTC_FS has a shorter runtime on all datasets than all baselines. In addition, the performance of the MTC_FS method is the most stable on the NBayes classifier.

Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods

Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficiency with which they can be used. A novel approach has been proposed for the early detection of BC by applying data mining techniques to the levels of prolactin (P), testosterone (T), cortisol (C), and human chorionic gonadotropin (HCG) in the blood and saliva of 20 women with histologically confirmed BC, 20 benign subjects, and 20 age-matched control women. In the proposed method, blood and saliva were used to categorize the severity of the BC into normal, benign, and malignant cases. Ten statistical features were collected to identify the severity of the BC using three different classification schemes—a decision tree (DT), a support vector machine (SVM), and k-nearest neighbors (KNN) were evaluated. Moreover, dimensionality reduction techniques using factor analysis (FA) and t-stochastic neighbor embedding (t-SNE) have been computed to obtain the best hyperparameters. The model has been validated using the k-fold cross-validation method in the proposed approach. Metrics for gauging a model’s effectiveness were applied. Dimensionality reduction approaches for salivary biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 66.67% to 93.3% and 90%, respectively, by utilizing t-SNE and FA. Furthermore, dimensionality reduction strategies for blood biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 60% to 80% and 93.3%, respectively, by utilizing FA and t-SNE. These findings point to t-SNE as a potentially useful feature selection for aiding in the identification of patients with BC, as it consistently improves the discrimination of benign, malignant, and control healthy subjects, thereby promising to aid in the improvement of breast tumour early detection.

Neighborhood multigranulation rough sets for cost-sensitive feature selection on hybrid data

Feature selection is a vital preprocessing step in real applications of data mining and machine learning. With the prevalence of high-dimensional hybrid data sets in real-world scenarios, along with the presence of test costs and misclassification costs, the need for effective feature selection methods has become more prominent. However, existing feature selection approaches mainly focus on cost-sensitive data from a single granularity perspective, and they are primarily applicable to single-typed data sets. To address these limitations, this paper presents a novel feature selection approach specifically designed for hybrid data, considering variable test costs and misclassification costs. The proposed method is based on neighborhood multigranulation rough sets (NMRS), which provides more comprehensive and multi-angle data analysis for cost-sensitive hybrid data. First, a novel multigranulation model is developed to effectively process cost-sensitive hybrid data. Building upon this model, a cost-based multi-criteria measure is proposed to evaluate the significance of features. This measure takes into account the comprehensive information of candidate features, including their power in algebraic view, information view, and associated costs. Furthermore, a heuristic feature selection algorithm based on NMRS is proposed to handle hybrid data with the test costs and misclassification costs. This algorithm leverages the benefits of the proposed multigranulation model and cost-based measure to identify the most discriminative features efficiently. Finally, the experimental results on twelve different datasets show that the proposed heuristic algorithm outperforms other compared algorithms, especially in total cost and classification accuracy.

Application of data mining to taxpayers issuing fictitious tax invoice using classification techniques

Pajak adalah tulang punggung penerimaan negara, namun penerimaan pajak tidak berjalan optimal karena adanya tindak pidana perpajakan. 44 % dari keseluruhan tindak pidana pajak berasal dari penerbitan Faktur Pajak Tidak berdasarkan transaksi sebenarnya. Hal tersebut mengurangi pendapatan dari sektor PPN bahkan dapat mengambil uang negara menggunakan restitusi PPN. Untuk menghadapi hal tersebut DJP dapat menggunakan data mining untuk melakukan audit yang lebih efektif dan efisien. Penelitian menggunakan metodologi CRISP-DM dan Python diterapkan kepada 1071 data wajib pajak penerbit dan 2142 wajib pajak bukan penerbit. Hasil dari penelitian memberikan model yang memiliki Prediction Efficiency sebesar 83,56%, reduction in Examination Effort sebesar 69,31%, dan Strike Rate sebesar 90,77%. Model tersebut kemudian dapat digunakan dengan Streamlit dan memprediksi 8 WP penerbit dengan probability 75% dari data deployment yang terdiri dari 1000 baris data.

Adaptive Fast XGBoost for Multiclass Classification

The popularization of sensoring and connectivity technologies like 5G and IoT are boosting the generation of data streams. Such kinds of data are one of the last frontiers of data mining applications. However, data streams are massive and unbounded sequences of non-stationary data objects that are continuously generated at rapid rates. To deal with these challenges, the learning algorithms should analyze the data just once and update their classifiers to handle the concept drifts. The literature presents some algorithms to deal with the classification of multiclass data streams. However, most of them have high processing time. Therefore, this work proposes a XGBoost-based classifier called AFXGB-MC to fast classify non-stationary data streams with multiple classes. We compared it with the six state-of-the-art algorithms for multiclass classification found in the literature. The results pointed out that AFXGB-MC presents similar accuracy performance, but with faster processing time, being twice faster than the second fastest algorithm from the literature, and having fast drift recovery time.

Design and realization of data mining simulation and methodological models

Data mining in education is steadily gaining momentum to explore the learning analytics because of its easy to use with any programming knowledge. This emerging method primarily focus on developing methods for handling and exploring voluminous information represented in distinct types of data. Deployment of data mining in education helps to better understand the synergy between the learning and educational settings established by the institutions to foster their learning activity. This work presents a data mining based virtual simulation experimental teaching system which provisions personalized interactive learning, real-world scene experimental training and comprehensive assessment of the learning effect. The proposed system employs interactive programming, automatic evaluation of experimental results, assessing the impact of teaching effectiveness with other modules. The work also provides a series of comprehensive cases and programming experiments apart from presenting the execution pages. The results indicate that this system can intuitively help students to improve their learning process, student participation and foster independent learning ability. The application of data mining alleviates the dependency on complex programming paradigms to make learning analytics, which is beneficial to both the teaching and learning communities.

Sales Product Clustering Using RFM Calculation Model And K-Means Algorithm on Primskystore

The optimization of data processing procedures can yield high-quality information that is then utilized by business owners in the decision-making process. An effective company plan, particularly in the realm of promotion, holds significant importance for store proprietors in order to foster sustained business expansion. Primskystore is an e-commerce platform operating inside the retail industry, where the efficacy of product sales promotion is perceived to be suboptimal. Sales product advertising continues to revolve around a single sort of product. The objective of this work is to develop a data mining model through the implementation of a web-based application that utilizes the K-means clustering approach and the RFM model. The utilization of the K-Means clustering approach and the RFM model can facilitate the clustering of sales products at retailers. The data mining application employed on this website utilizes the Python programming language, MySQL as the database, and the Unified Modeling Language for system model creation. The findings of this research encompass the development of a web-based data mining tool designed to present the outcomes of product sales clustering within retail establishments. The system has the capability to present the outcomes of the RFM calculation model and KMeans clustering. Specifically, it reveals the presence of three distinct clusters, with cluster 0 accounting for 30% of the data, cluster 1 representing 7.5% of the data, and cluster 2 encompassing 62.5% of the data. This web-based data mining program has the potential to assist retailers in determining an optimal promotional business strategy.

Penerapan Data Mining Untuk Mengetahui Minat Siswa Pada Pelajaran IPA Mengunakan Metode K-Means Clustering

The purpose of this research is to build a data mining application to find out students' interest in science subjects at SMPN 18 Bengkulu City. With data collection techniques by way of observation and interviews, this study used a sample of data on grades IX.8 science subjects with a total of 30 students at SMPN 18 Bengkulu City. The research method used is the K-Means Clustering method because this method separates data into groups that have objects with similar characteristics. In building this application using Xampp server, PHP programming language and MySQL database. This datamining application is used to make it easier for a teacher or admin in this application to manage student data to determine student interest in science subjects. The results of this study were 3 groups of students' interest in science subjects, namely 12 students with high interest, 17 students with moderate interest and 1 student with low interest in SMPN 18 Bengkulu City.

Dynamics of pesticides in surface water bodies by applying data mining to spatiotemporal big data. A case study for the Puglia Region

Surface water pollution by pesticides is a primary concern in many parts of the world. Therefore, an effective monitoring program is essential to assess the environmental state of aquatic ecosystems and evaluate mitigation strategies.In the Puglia Region (southern Italy), a complex seasonal monitoring program for detecting pesticide residues in water was initiated in 2018 and is still underway (over four years). The program was based on site-specific assessments and identified 170 Plant Protection Products (PPPs) to identify residues in the surface water bodies of Puglia.In this context, the present work aims to analyse pesticide data obtained from the regional monitoring of rivers over four years using a multidisciplinary approach, which includes statistical methods, data mining, and mapping tools to extract as much information as possible.To this end, data mining was applied to identify the prevalent mixtures of pesticide residues found at the monitoring stations and correlate these results with possible causative factors. The results showed that surface water bodies are subject to different pressures derived from the massive use of PPPs, and several seasonal and territorial-related factors were identified to be strictly correlated with pesticide concentration results. Nine main PPPs mixtures have been identified in the Puglia River. Glyphosate, AMPA, imidacloprid, and azoxystrobin represent the main residues detected in the surface aquatic environments regarding the amount and frequency revealed.The methodological approach proposed in the present work can represent a good “model study” to be used by researchers to interpret water quality trends and data variability from long-term monitoring studies. Moreover, our results can significantly support the decision-making process and implementation of environmental mitigation measures by optimising the results of complex monitoring programs.

Application of IoT technology on control system and monitoring for Cucumis Melo L. grown in greenhouse

In this paper, we propose developing a system for optimally watering Cucumis Melo L. crops based on an application Internet of Things (IoT). The three components are hardware, a web application, and a mobile application. The first component was designed and implemented in control box hardware connected to collect crop datas. Soil moisture sensors are used to monitor the greenhouse, connected to the control box. The second component is a web-based application that was designed and implemented to manipulate the crop data and field information. This component applied data mining to analyze the data for predicting suitable temperature, humidity, and soil moisture for optimal future management of crop growth. The final component is mainly used to control crop watering through a mobile application on a smartphone. This allows either automatic or manual control of the user. The automatic control uses data from soil moisture sensors for water. The results showed the implementation to be useful in agriculture. The moisture content of the soil was maintained appropriately for Cucumis Melo L. growth, reducing costs and increasing agricultural productivity. Moreover, this work represents improvements to agriculture through digital innovation.

PENERAPAN DATA MINING DENGAN METODE NAIVE BAYES CLASSIFIER PADA PENJUALAN BARANG UNTUK OPTIMASI STRATEGI PEMASARAN

Along with technological developments, market developments are also increasing rapidly and competition is starting to occur between entrepreneurs and business people, which requires entrepreneurs to take advantage of existing technology. At this time the Universal Homeware Store does not realize that this data can be processed effectively to find out the development of a business or business that is being run, besides that the procurement of goods that customers need is often unavailable. One of the data processing technologies is by applying data mining. Data mining is a form of extracting information from data that is large enough to obtain data patterns so as to produce information data that is useful for making decisions and increasing profits in developing marketing strategies. One of the uses of data mining is using the Naive Bayes Classifier algorithm. Naive Bayes Classifier is one of the existing algorithms in data mining which is used to deal with uncertainty problems by emphasizing the concept of probability of use and measuring the level of accuracy so that it can predict purchasing strategies from past sales analysis. with research results Using 36 data and with rapid minner tools produces an accuracy value of 80%

PENDEKATAN DATA MINING UNTUK MEMILIH PRODUK TERLARIS MENGGUNAKAN ALGORITMA NAIVE BAYES

Technological advances today can be exploited to process data into more useful information. In data collection, information collection is especially useful to maximize profits and develop marketing strategies. One way to increase profits is by using data mining techniques to help business actors in making decisions about stocks, increased profits and more. The Matahari Department Store is the largest retail platform in Indonesia, one of the retail stores located in Batam is the Matahari Department store Nagoya Hill Batam. The transaction data on the store that is still processed does not use a method that causes the processing of product sales data to be less effective and less efficient. Seeing from the number of transactions, a system is needed to predict the sale of the best-selling product as long as it can determine the correct stock for the products sold and can increase the profit, sale and purchase of the product. This research was conducted with the aim of applying data mining methods using the Naive Bayes Classifier algorithm to select the best-selling products in the outlet of the Matahari Nagoya Hill Batam Department Store. By using the collected sales data, the system is expected to increase profits steadily and avoid shortages of product stocks. Through analysis using the Naive Bayes Classifier method, the study achieved an accuracy of 67% and obtained a bag sales result to be the best-selling sale during January 2023 through March 2023 with a sales percentage of 20%.

Colorectal Cancer Classification from Protein Sequences Using Several RNN Pre-Trained Models

Bioinformatics is one field that can integrate health information with data mining applications in order to predict or analyze the patient’s information. One among the several diseases is colorectal cancer,which is horrible and rated the third leading disease in the area of cancer which leads to death in both men and women.In general, there are several intelligent methods to identify several kinds of problems in gene selection for predicting cancer,but there is no method to give a solution for patients who are diagnosed in the advanced stage.This motivated me to design theproposed approach in which we try to identify all the homo protein sequences and then train these sequences corresponding to one which causes colorectal cancer.InBioinformatics,a protein acts as one of the main agent or source to perform a biological function by interacting with molecules like Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA). The function of a protein determines the healthy or diseased states of an organism. Protein interaction with other proteins can be visualized through a network called Protein-Protein Interaction Network (PPIN).In general classification of protein sequences is a very complex task. Deep learning techniques like CNN and RNN can be used to solve the problem.In computational bioinformatics,the classification of protein sequence plays an important role in determining accuracy.To improve the accuracy of our current model, the suggested method incorporates GRU, LSTM, RNN, and Customized LSTM into an RNN based architecture by optimizing the parameters in a two-way direction.Here we try to test all the models on sample protein sequences that are collected from TCGA and then determine the correctness of testing data and training data.