Data Mining Clustering Techniques Research Articles

Application of Data Mining to Measure the Effectiveness of the Islamic Boarding School’s Independent Curriculum based on Learning Achievement using the Clustering Method

The evolution of educational curricula has been a focal point for institutions aiming to enhance learning outcomes and adapt to students' diverse needs. In this context, Islamic boarding schools, or pesantren, are increasingly exploring independent curricula to better serve their students. This research aims to measure the effectiveness of the independent curriculum at the Al Binaa Bekasi Islamic Boarding School, especially regarding learning achievements in general and Islamic subjects. The method used is data mining clustering to analyze student learning achievement data. In the initial stage, the data collected includes student scores in general subjects (such as Islamic Religious Education, Pancasila Education, Indonesian, English, Mathematics, Science, Social Sciences, Arts, Sports, ICT, Sundanese) and Syar'i (Quran tajwid, hadith, aqidah, fiqh, Hadassah, short). Then, data mining clustering techniques are used to group students based on their achievements in the two subjects. The results of the analysis show that the independent curriculum at Al Binaa Islamic Boarding School effectively increases student learning achievement. The groups formed from data mining clustering show patterns consistent with curriculum objectives, where students in the same group have similar levels of achievement in general and star subjects. This indicates that the independent curriculum has succeeded in leveling student learning achievement. This research contributes to understanding the effectiveness of the independent curriculum in Islamic boarding schools. It can be a basis for further development in designing Islamic boarding school education curricula that are more adaptive and responsive to student needs.

Student Performance Prediction Using Machine Learning Algorithms

Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university’s ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students’ performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies’ Bouldin method, obtains clusters to find important features affecting students’ performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model’s prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features.

Veri Madenciliğinde Kümeleme Teknikleri: Yöntemler, Zorluklar ve Uygulamalar Üzerine Bir Araştırma

Clustering is a crucial technique in both research and practical applications of data mining. It has traditionally functioned as a pivotal analytical technique, facilitating the organization of unlabeled data to extract meaningful insights. The inherent complexity of clustering challenges has led to the development of a variety of clustering algorithms. Each of these algorithms is tailored to address specific data clustering scenarios. In this context, this paper provides a thorough analysis of clustering techniques in data mining, including their challenges and applications in various domains. It also undertakes an extensive exploration of the strengths and limitations characterizing distinct clustering methodologies, encompassing distance-based, hierarchical, grid-based, and density-based algorithms. Additionally, it explains numerous examples of clustering algorithms and their empirical results in various domains, including but not limited to healthcare, image processing, text and document clustering, and the field of big data analytics.

A data-mining approach to understanding the impact of multi-doping on the ionic transport mechanism of solid electrolytes materials: the case of dual-doped Ga0.15/Scy Li7La3Zr2O12

Uncovering Ga/Sc dual-doping effects on Li-ion dynamics in LLZO through molecular dynamics, Monte Carlo simulations, and data mining clustering techniques.

Penerapan Metode Clustering Dalam Segmentasi Pelanggan Perusahaan Logistik

Marketing is important in business to compete and maintain market share. The development of technology brings major changes in the industry. In addition to product development as well as required services, and customer segmentation becomes a factor to consider in marketing strategies. Clustering, such as the K-Means method, is used in customer segmentation to divide data into groups based on similarities. This technique helps in useful pattern recognition and customer segmentation. By applying Clustering techniques in Data mining, companies can understand customer behavior, recognize similar customer groups, and plan marketing strategies accordingly. The results showed that the best cluster was generated with a k value of 4, and the data was normalized using the Min-Max Normalization method. Grouping customers in the form of clusters can enable the identification of consumer profiles to guide companies in decision making.

New Pre-Processing Approach Based on Clustering Users Traces According to their Learning Styles in Moodle LMS

Nowadays, many Moroccan universities and institutions start offering training and online courses "E-learning". Which accumulate a vast amount of information that is very valuable for analyzing students’ behavior and could create a gold mine of educational data. However, handling the vast quantities of data generated daily by the learning management systems (LMS) such as Moodle has become more and more complicated. This massive data can be used to improve decision making and management, which requires a proper extracting and cleaning methods.
 The purpose of this paper is to suggest a new approach for the preprocessing of the execution traces generated during the interaction of learners with the Moodle LMS and especially the educational content in SCORM format. 
 In this study, we built two experimental corpus with the learning platform Moodle. Using the data generated by the experimental corpus, we applied the Clustering data mining technique as a preprocessing step in our process discovery. Hence, students with similar learning styles or performance levels are grouped together which should help us to build a partial process model (learning process) that are easier to understand for the decision makers.

Segmentation K-Means Clustering Model With SPSS Program Case Study Customer the Park Mall Sawangan

In a transitioning word, specially in mall property industry, a winning strategy comes from anticipating with people needs and desires, understanding them in the most holistic way possible. The 7P marketing mix refined by Booms and Bitner in 1981 which consists of elements of Product, Price, Place, Promotion, People, Physical evidence, Processes and additional Partnerships, is a tool for analyzing existing market conditions in more depth. In addition, to meet customer wants, needs, and satisfaction refers to the market environment, both external and internal. This research succeeded in modeling customer segmentation based on clustering data mining techniques with K-Means analysis using the SPSS program. Based on the analysis results obtained, there are 3 clusters, namely clusters: consumptive customer, potential customer, and standard young customer. From the results of this study, we get the personas that emerge as a result of this clustering analysis, and identify the needs, desires, and preferences of the cluster that is formed. We choose to develop tactic 8Ps marketing mix for cluster potential customer.

Pengelompokan Cuaca Kota Palembang Menggunakan Algoritma K-Means Clustering Untuk Mengetahui Pola Karakteristik Cuaca

Weather related information is one of the things that is very important and has a big influence on all kinds of life activities such as in public safety, socio-economics, agriculture, aviation, and so on.The weather in each place or region is different, this happens because of the different weather elements in each place/region. By using data mining clustering techniques, weather clustering will be carried out in the city of Palembang. K-means is the algorithm chosen for clustering the weather in the city of Palembang. The test was carried out using daily weather data for 2020-2021 from BMKG by utilizing rapidminer application as learning techniques for data. So that we will get a group of weather characteristics of Palembang city based on similarities and dissimilarities. From the test results, the best k was obtained at k=3 with the parameters Measure Types ( NumericalMeasure ) and Divergences ( DynamicTimeWarpingDistance ) as well as a local random seed of 2500 seen from the results of the Davies-Bouldin Index (DBI). This weather grouping can later provide information on how the weather character is and reduce the impact of sudden changes in weather conditions.

Pattern mining algorithms for data streams using itemset

Knowledge discovery and data mining are fast growing fields of study that span a variety of disciplines, including distributed systems, databases, artificial intelligence, visualization, statistics, high-performance computing, and parallel computing. Raw data is collected by people in business, science, medicine, academia, and government, and there are various commercial programmes that process the data to provide general and specific purpose knowledge discovery. “Turn Data into Knowledge” is an important goal in knowledge discovery and data mining. Data mining is the process of examining data previously stored in databases in order to solve problems. The technique of detecting patterns in vast data repositories is known as data mining. In order to complete a data mining task, effective exploratory strategies are always required. Association rules, correlations, sequential patterns, classification, clustering, and other data mining techniques are only a few examples. Every technique has its own value, which is determined by the application area and challenges for which it is used. The objectives of this study, as stated in the synopsis, are met using association rule mining methodology. The original motivation for association rules mining was the problem of supermarket transaction data. The problem with supermarket transaction data is that it is used to investigate client purchasing habits. The frequency with which things have been purchased together is described by association rules. For instance, the association rule “cool drink=> chips (80%)" implies that 80% of customers who purchase cool drink also purchase chips. Such criteria can be beneficial in making judgments about store layout, product pricing, and marketing, among other things.

Seed Community Identification Framework for Community Detection over Social Media.

The social media podium offers a communal perspective platform for web marketing, advertisement, political campaign, etc. It structures like-minded end-users over the explicit group as a community. Community structure over social media is the collaborative group of globally spread users having similar interests regarding a communal topic, product or any other axis. In recent years, researchers have widely used clustering techniques of data mining to structure communities over social media. Still, due to a lack of network and implicit communal information, researchers cannot bind mutually robust and modular community structures. The collaborative features of social media are inherent with implicit and explicit end-users. The explicit nature of both active and passive users is easily extracted from the graphical structure of social media. On the other hand, the degree of information inclusion of implicit features depends upon end-users participation. The Implicit features of frequently active users are diversely available, while integrating passive and silent users’ implicit features over the community is tedious. This work proposed a social theory based influence maximization (STIM) framework for community detection over social media. It combines user-generated content with profile information, extracts passive social media users through influence maximization, and provides the user space for influencing inactive users. The STIM framework clusters identical nodes over the maximum influencing node axis based on their graphical parameters such as node degree, node similarity, node reachability, modularity, and node density. This framework also provides the structural, relational and mathematical concept for the functional grouping of like-minded people as a community over social media through social theory. Finally, an evaluation has been carried out over six real-time datasets. It analyses that convolution neural network over STIM structure more dense and modular communities via influence maximization. STIM acquired around 93% modularity and 94% Normalized Mutual Information (NMI), resulting in approximately 2.23% and 5.69% improvements in modularity and NMI, respectively, over the best-acquired result of the benchmark approach.

Customer segmentation using k-means clustering for developing sustainable marketing strategies

Sales and marketing is the indispensable department of an organization which leads to the generation of revenue and building customer relationship. Marketing is the process of finding the potential customers and sales is the process of converting those potential customers into real customers. Hence, it is imperative that marketing and sales go hand in hand. Developing marketing strategies needs proper market research which can cover the relevant pointers like demographics, culture, spending power, income and many more. The process of segmentation, targeting and positioning (STP) is carried out to develop marketing and sales strategies. STP is done by collection of the marketing intelligence. For this process, surveys are also used but data mining has far more effective and better results so far. Organizations tend to take risk because of the importance and relevance of the marketing and sales department. Most of the budget in the organizations is allocated for marketing and promotional activities. For making data-driven and accurate decisions, data mining is used in various fields to extract valuable information and patterns. This paper discusses the use of the data mining concept on marketing. This paper aims to analyze marketing data with k-means data mining clustering techniques and to find the relationship between marketing and k-means data mining clustering techniques.

Clustering Mining Equipment Productivity Data Using K-Means Algorihtm

There are many factors that affect the productivity of mining equipment, one of which is the condition of the mining equipment when it is used. Knowing the condition of the machines on mining equipment is mandatory for supervisors in mining activities. In analyzing data on the condition of mining equipment in large quantities requires a lot of energy and time, so a classification model is needed that can categorize the mining equipment based on its performance. Data Mining is a process that uses statistical, mathematical, artificial intelligence and machine learning techniques to extract information. The results of data mining for mining productivity data are obtained duration of mining equipment work has a impact on productivity based on correlation value between work duration and maintenance duration of a tool. Data mining analysis is also carried out by clustering mining equipment using the K-Means model. The results obtained a conclusion that supports mining equipment with less working duration affects to productivity. The result shows variables that affect the productivity of mining equipment and divide mining equipment categories based on tool performance with data mining clustering techniques.

Implementasi Data Mining untuk mengelompokkan Pasien Rawat Jalan dan Rawat Inap Asuransi Kesehatan

Patient Visits Outpatient and inpatient insurance at Class C Hospitals is increasing from year to year. Increased visits to insurance patients will have an impact on the inpatient and outpatient health services provided. From the increase in patient visits, the data owned by the hospital is increasingly abundant. The data can be used to explore knowledge, find certain patterns.
 To explore knowledge about Inpatient and Outpatient Insurance patients, data mining clustering techniques are used with the Self Organizing Map (SOM) algorithm using R Studio tools. Clustering technique with the implementation of the Self Organizing Map (SOM) algorithm is a technique for grouping data based on certain characteristics which are then mapped into areas that resemble map shapes. The CRISP-DM method is used in this study to perform the stages of the data mining process.
 The results obtained from the implementation of clustering with the Self Organizing Map (SOM) algorithm are obtained 2 clusters representing dense areas and non-congested areas. Dense areas are represented by Internal Medicine Clinic, Surgery Clinic, Eye Clinic, Hemodialysis, Melati Room, Orchid Room, Bougenville Room, Flamboyan Room. Non-crowded areas are represented by General Clinics, Dental Clinics, Obstetrics and Gynecology Clinics, Children's Clinics, Mawar Room and Soka Room

Chemical similarity methods for analyzing secondary metabolite structures

Abstract The relation that exists between the structure of a compound and its function is an integral part of chemoinformatics. The similarity principle states that “structurally similar molecules tend to have similar properties and similar molecules exert similar biological activities”. The similarity of the molecules can either be studied at the structure level or at the descriptor level (properties level). Generally, the objective of chemical similarity measures is to enhance prediction of the biological activities of molecules. In this article, an overview of various methods used to compare the similarity between metabolite structures has been provided, including two-dimensional (2D) and three-dimensional (3D) approaches. The focus has been on methods description; e.g. fingerprint-based similarity in which the molecules under study are first fragmented and their fingerprints are computed, 2D structural similarity by comparing the Tanimoto coefficients and Euclidean distances, as well as the use of physiochemical properties descriptor-based similarity methods. The similarity between molecules could also be measured by using data mining (clustering) techniques, e.g. by using virtual screening (VS)-based similarity methods. In this approach, the molecules with the desired descriptors or /and structures are screened from large databases. Lastly, SMILES-based chemical similarity search is an important method for studying the exact structure search, substructure search and also descriptor similarity. The use of a particular method depends upon the requirements of the researcher.

Implementation of clustering unsupervised learning using K-Means mapping techniques

The manufacturing sector is one of the major contributors to the Indonesian economy. Human work is still needed on the production floor in the manufacturing industry to ensure a smooth operation. This study explores the use of unregulated learning clustering techniques in data mining in the form of clusters of employees in the production industry. The data collection process is carried out through a survey conducted by the Central Statistical Agency (abbreviated as the BPS) with Url: https:/www.bps.go.id in Large Medium Industries and Micro & Small Industries. The statistics used include 24 industrial classifications, with the number of manufacturing employees in the 2017-2019 industry as a percentage. The unregulated technique of learning clustering is k-means. The Large Cluster (E1) and the Low Cluster are the two labels used (E2). The Davies Bouldin Index (DBI) parameter with a dbi value of 0,929 was used to evaluate the cluster (k=2). The findings showed five manufacturing sectors of the high cluster in 5 cluster and 19 manufacturing sectors of the small cluster in 0 cluster. For each cluster the centroid value is 1.67; 1.64; 1.592 (cluster 1/E1) and 0.348; 0.343; 0.3447 (cluster 0/E2), respectively. The research findings will inform the government to improve labour absorption, which will reduce the unemployment rate by substantial numbers in each manufacturing industry.

Assessment of the Accounting Regulations in the Republic of Serbia: Application of Cluster Analysis

Abstract The focus of the paper is on identification of hidden patterns in the opinion of accounting professionals, in order to identify the deficiencies in the legal accounting regulations and to examine their perspective on international and legal accounting regulations. The data collected through the questionnaire was divided using clustering data mining technique. We identified different groups of accountants with specific patterns regarding the expressed attitudes towards both regulations, as well as different groups of accountants with specific patterns regarding the way of obtaining information. The findings point to the prevailing negative attitude towards the legal accounting regulations quality, and differences in the habits and attitudes of certified and non-certified accountants, i.e. accountants who (don’t) use International Financial Reporting Standards. Since there are four developed models, we used them as a basis for proposing a model of information seeking that could mitigate negative views on accounting regulations in the national economy. The key points of the proposed model are the introduction of compulsory continuous professional education and certification of all accountants, as well as activities for the popularization and accessibility of the international accounting regulations.

FIXED VOTERS CLUSTERING TO DETERMINE THE LEVEL OF BEGINNER VOTERS USING DATA MINING TECHNIQUES

Data mining clustering technique is used to classify the level of beginner voters using the K-Means method. Fixed voter clusters are used for decision making for stakeholders regarding information on beginner voters in each district and sub-district. The error calculation method is used to measure the level of error value for each distance calculation used, the distance calculation method used ie Euclidean, Manhattan, and Minkowski Distance with the Means Square Error (MSE) approach to measure the level of the error value. The calculation results show that the lowest error occurs in the calculation of the Minkowski Distance model 3 cluster, where the error rate is 11%, while the highest error rate occurs in the calculation of the Manhattan Distance model 5 cluster, which is 38%.

Detailed Study of Clustering Technique In Data Mining with Principle of Data Mining

Clustering technique in data mining is a main approach to deal with the data an extraction of useful patterns and knowledge from it. Clustering is involved in the datamining process. Datamining is the way of pulling out the knowledge, information, useful patterns and a reliable data from a huge gigantic amount of raw data as per the needs of the targeted sector. In technical aspects the Data Mining is a way of finding out the useful patterns from the raw data by using the suitable techniques of statistics, Machine learning, and Database techniques. Data mining target two major aspects of extraction of meaning full pattern data for concern of large-scale for better understanding of shapes and profitable patterns of data which impacts globally and the other is small-scale which deals with the lesser impact on the global scale. This paper give a brief overview of Clustering technique under the Data mining process their features and functionality. Majorly concentrate on Clustering technique and their algorithms with the pro’s & con’s and understand the need of clustering and its importance in Data mining process. The Data mining principle is also explained briefly just to build a base to understand the techniques and their importance which has to be discussed

Rural Health Unit Decision Support System with Mapping

In this highly challenging and demanding world, presence of data and technology are overwhelming. But at present, some institutions still engage in manual-type of operations like the Rural Health Unit of the Municipality of Solano, Nueva Vizcaya. Problems, issues and challenges encountered by the unitin the delivery of its medical servicesand the extent of compliance in ISO/IEC 25010 Software Quality Standards were identified. And with the uncontrollable availability of data, these can be handled and treated using data mining techniques to predict disease occurrences. In this study, the clustering and classification data mining techniques were utilized in order to predict disease occurrences of every barangay of the municipality at a given time. An efficient record management system along with a decision support system was developed to meet the challenges of the unit. It mainly features the disease-occurrence mapping to assist physicians and other health professionals in the unit in their decision-making tasks particularly in diagnosis, treatment and recommendations. In terms of ISO/IEC 25010 Software Quality Standards, the system gained a “very great extent” qualitative rating.

SEGMENTASI PELANGGAN MENGGUNAKAN METODE K-MEANS CLUSTERING BERDASARKAN MODEL QRF PADA PERUSAHAAN RINTISAN PENYEDIA TENAGA KERJA

The difficulty of getting a job that is in accordance with the interests and specialization of a worker, as well as the difficulty of the company getting a worker who suits the needs of the company causes the mushrooming of consulting firms or labor providers in Indonesia today. With the increasing number of companies providing labor, of course the competitiveness of the business industry in the human resources is increasingly high. So it needs to be analyzed to determine the right business strategy, such as determining the company's promotion goals. One of them is analyzing the segmentation of customers who have worked together. This research successfully modeled customer segmentation based on data mining clustering techniques using the K-Means data mining algorithm. The QRF (Quantity, Recency, Frequency) modeling process is analyzing the customer's behavior from the number of requests in each transaction carried out within a certain timeframe, as well as recency as the identification of the time span of the last transaction, as well as the number of transactions made within a certain time period. Researchers conducted a period of data for one year by analyzing customer activity in start-up providers of labor during 2019, on 86 active customers. Based on the analysis results obtained, customer segmentation in two clusters with QRF (Quantity, Recency, Frequency) modeling using Davies Bouldin Index (DBI) evaluation scored -0,482, while customer segmentation in three clusters using QRF (Quantity, Recency, Frequency) evaluation using Davies Bouldin Index (DBI) evaluation to obtain -0.469, and customer segmentation in four clusters with QRF (Quantity, Recency, Frequency) modeling using Davies BouldinIndex (DBI) evaluation to obtain -0,526.
 Keywords— pelanggan, clustering, algoritma k-means, DBI, QRF