K Means Clustering and Meanshift Analysis for Grouping the Data of Coal Term in Puslitbang tekMIRA

Indonesian government agencies under the Ministry of Energy and Mineral Resources have problems in classifying data dictionary of coal. This research conduct grouping coal dictionary using K-Means and MeanShift algorithm. K-means algorithm is used to get cluster value on character and word criteria. The last iteration of Euclidian distance calculation data on k-means combine with Meanshift algorithm. The meanshift calculates centroid by selecting different bandwidths. The result of grouping using k-means and meanshift algorithm shows different centroid to find optimum bandwidth value. The data dictionary of this research has sorted in alphabetically

Review of Technique and Algorithm for Educational Data Mining: Trend and Challenge in Games Design

This study reviews techniques and algorithm models often used in the analysis of educational data mining. The review in this study is based on previous studies to provide researchers knowledge about trends and challenges analysis Educational data mining in game design meaningful. However, there is a lot of games design developed without analysis Educational data mining which then will not answer the student problem. The analysis needed periodic data and developing the game required actual student conditions, this is a combination inseparable. Determine Research questions, Search Terms, and filtering for the selection and analysis of the article review. There are some student problems on analysis review, namely prediction student performance, student behavior, student at-riks, and student dropout. The number of Articles in the study was 33 with 21 Articles of research and 12 of Article review. The number of studies 8 with percent 38% used techniques Confusion matric with 33% percent used algorithms Decision Tree in 7 of studies. The section in this study consists of techniques evaluation, model selection, outcome, subject, and algorithm method. Which are recommended techniques and algorithms for analysis Educational data mining and in ideal game design to further research

Collaboration FMADM And K-Means Clustering To Determine The Activity Proposal In Operational Management Activity

Indonesian government agencies under the Ministry of Energy and Mineral Resources still use manual methods in determining and selecting proposals for operational activities to be carried out. This study uses the Decision Support System (DSS) method, namely Fuzzy Multiple Attribute Decision Decision (Fmadm) and K-Means Clustering method in managing Operational Plan activities. Fmadm to select the best alternative from a number of alternatives, alternatives from this study proposed activity proposals, then ranking to determine the optimal alternative. The K-Means Clustering Method to obtain cluster values for alternatives on the criteria for activity dates, types of activities, and activity ceilings. The last iteration of the Euclidian distance calculation data on k-means shows that alternatives that have the smallest centroid value are important proposal criteria and the largest centroid value is an insignificant proposal criteria. The results of the collaboration of the Fmadm and K-Means Clustering methods show the optimal ranking of activities (proposal activities) and the centroid value of each alternative

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.Comment: 25 pages, 5 figure

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

Analyzing Web Server Access Log Files Using Data Mining Techniques

Nowadays web is not only considered as a network for acquiring data, buying products and obtaining services but as a social environment for interaction and information sharing. As the number of web sites continues to grow it becomes more difficult for users to find and extract information. As a solution to that problem, during the last decade, web mining is used to evaluate the web sites, to personalize the information that is displayed to a user or set of users or to adapt the indexing structure of a web site to meet the needs of the users. In this work we describe a methodology for web usage mining that enables discovering user access patterns. Particularly we are interested whether the topology of the web site matches the desires of the users. Data collections that are used for analysis and interpretation of user viewing patterns are taken from the web server log files. Data mining techniques, such as classification, clustering and association rules are applied on preprocessed data. The intent of this research is to propose techniques for improvement of user perception and interaction with a web site

Optimization Modeling and Machine Learning Techniques Towards Smarter Systems and Processes

The continued penetration of technology in our daily lives has led to the emergence of the concept of Internet-of-Things (IoT) systems and networks. An increasing number of enterprises and businesses are adopting IoT-based initiatives expecting that it will result in higher return on investment (ROI) [1]. However, adopting such technologies poses many challenges. One challenge is improving the performance and efficiency of such systems by properly allocating the available and scarce resources [2, 3]. A second challenge is making use of the massive amount of data generated to help make smarter and more informed decisions [4]. A third challenge is protecting such devices and systems given the surge in security breaches and attacks in recent times [5]. To that end, this thesis proposes the use of various optimization modeling and machine learning techniques in three different systems; namely wireless communication systems, learning management systems (LMSs), and computer network systems. In par- ticular, the first part of the thesis posits optimization modeling techniques to improve the aggregate throughput and power efficiency of a wireless communication network. On the other hand, the second part of the thesis proposes the use of unsupervised machine learning clustering techniques to be integrated into LMSs to identify unengaged students based on their engagement with material in an e-learning environment. Lastly, the third part of the thesis suggests the use of exploratory data analytics, unsupervised machine learning clustering, and supervised machine learning classification techniques to identify malicious/suspicious domain names in a computer network setting. The main contributions of this thesis can be divided into three broad parts. The first is developing optimal and heuristic scheduling algorithms that improve the performance of wireless systems in terms of throughput and power by combining wireless resource virtualization with device-to-device and machine-to-machine communications. The second is using unsupervised machine learning clustering and association algorithms to determine an appropriate engagement level model for blended e-learning environments and study the relationship between engagement and academic performance in such environments. The third is developing a supervised ensemble learning classifier to detect malicious/suspicious domain names that achieves high accuracy and precision

Some Contribution of Statistical Techniques in Big Data: A Review

Big Data is a popular topic in research work. Everyone is talking about big data, and it is believed that science, business, industry, government, society etc. will undergo a through change with the impact of big data.Big data is used to refer to very huge data set having large, more complex, hidden pattern, structured and unstructured nature of data with the difficulties to collect, storage, analysing for process or result. So proper advanced techniques to use to gain knowledge about big data. In big data research big challenge is created in storage, process, search, sharing, transfer, analysis and visualizing. To deeply discuss on introduction of big data, issue, management and all used big data techniques. Also in this paper present a review of various advanced statistical techniques to handling the key application of big data have large data set. These advanced techniques handle the structure as well as unstructured big data in different area