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37 articles for “data clustering”
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Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations
Abstract: K-Means clustering is a widely used unsupervised learning algorithm for partitioning a dataset into distinct clusters. Despite its popularity and simplicity, K-Means has several limitations, such as sensitivity to initial centroids, convergence to local minima, and inefficiency with large datasets. This paper reviews recent advancements aimed at addressing these challenges and enhancing the performance of the K-Means algorithm. Innovations include improved initialization methods, such as K-Means++, which significantly reduce the …
Published in International Journal of Solid State Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 30–37 Read article
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Effects of Cluster Computing on Big Data Analysis and Network Topology
Abstract: The rapid expansion of big data has posed substantial difficulties for conventional computing systems. As a result, cluster computing has grown to be a potent method for effective large data processing. Cluster computing involves multiple interconnected nodes functioning as a unified system, pooling together their processing, storage, and memory resources. These nodes are typically connected through high-speed networks such as ethernet or InfiniBand, facilitating efficient data sharing and communication among …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 1, Issue 1, 2023 · pp. 31–39 Read article
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Efficient Clustering Techniques for Data Stream Mining
Abstract: Data mining mainly works on a massive database for storing heavy amount of data. It is generally essential for extracting the meaning insights from the massive, continuously growing database. The traditional method often struggles with sheer volume and the dynamic nature of the modern data. Data stream mining allows for the real-time analysis, means insights are generated as the data arrives, and not after the long batch process. This continuous …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 26–32 Read article
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Application of Proportional-Share with Punishment Principle for Resource Sharing in Parallel Computing Applications
Abstract: Efficient resource sharing is a cornerstone of high-performance parallel computing. While proportional-share scheduling has long been a foundational approach for distributing resources according to predefined weights, its effectiveness can be compromised by tasks that over-consume their allocated share, leading to system-wide performance degradation and unfairness. This review article investigates the application of the “Proportional-Share with Punishment” (PSWP) principle, a hybrid scheduling paradigm designed to address this challenge. PSWP integrates the …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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Classification and Detection of Brain Tumor using Convolutional Neural Network
Abstract: Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient's standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article
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Evaluation of Machine Learning Classifiers for Sentiment Analysis
Abstract: Sentiment in social media refers to users’ emotions and opinions through their posts and interactions. Sentiment analysis (SA) refers to relating and classifying the sentiments expressed as engagement and interactions between users. When analyzed, tweets frequently produce a large source of clustered data. These data help determine people’s opinions about a variety of motifs. Thus, this study presents an Automated Machine Learning (ML) Sentiment Analysis Model to detect media sentiment. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 141–154 Read article
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Big Data, Big Impact: The Role of Analytics in Modern Business
Abstract: In modern business, “Big Data” signifies the vast amount of data collected from various sources, and “Big Data Analytics” refers to the process of analyzing this data to extract valuable insights, enabling companies to make data-driven decisions, optimize operations, better understand customers, and ultimately gain a competitive edge by identifying trends, patterns, and opportunities that might otherwise be missed. This study analyzes large datasets, by which businesses can gain deeper …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 2, 2025 · pp. 01–11 Read article
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Intrinsic Evaluation of Graph Embeddings: Assessing Clustering and Community Detection Performance
Abstract: This paper presents an intrinsic evaluation of some graph embedding techniques on clustering and community detection tasks. We analyze a diverse set of embedding methods, ranging from traditional techniques such as Laplacian eigenmaps to more recent approaches like graph autoencoders, high-order proximity preserved embedding (HOPE), and graph attention network (GAT), using two widely studied datasets, Cora and CiteSeer. Our evaluation relies on two main metrics: Silhouette score with respect to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 40–48 Read article
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An Approach for Travel Pattern Analysis Using HDBSCAN and Apriori Algorithms
Abstract: Most mega-city regions around the world are suffering from an ongoing increase in the number of commuting trips. Understanding commuting patterns is crucial for both public and authority planners. The understanding of travel patterns helps passengers to know about the places and the time where they could get vacant transport and also helps authority planners in laying out a new transport service. The traditional way of understanding travel patterns includes …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 1, Issue 2, 2023 · pp. 1–9 Read article
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Improving Node Efficiency in Wireless Sensor Networks Using Advanced Clustering and Routing Techniques
Abstract: Many Internet of Things (IoT) applications, such as disaster relief, smart buildings, smart farming, and healthcare monitoring, have made use of wireless sensor networks (WSN). It is one of the alternatives to address different IoT difficulties in different contexts. One of the main problems with sensor networks is power efficiency. WSN operated on the Client-Server (CS) architecture in the past, however researchers suggested Mobile Agent (MA) based WSN to increase …
Published in Recent Trends in Sensor Research & Technology · Vol. 11, Issue 2, 2024 · pp. 35–64 Read article
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Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article
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Passenger car fuel economy: Insights from big data analytics
Abstract: The increasing availability of real-world vehicle telematics through On-Board Diagnostics II (OBD-II) systems has enabled data-driven evaluation of passenger car fuel economy beyond conventional laboratory-based test cycles. While standardized certification procedures ensure repeatability, they often fail to capture the influence of real-world traffic conditions, driver behaviour, and transient vehicle operation. This study presents a structured Big Data Analytics (BDA) approach for analysing high-frequency OBD-II data collected from a gasoline passenger …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 8–19 Read article
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Eye Disease Classification Using K-means Clustering Algorithm and Ensemble Classification Approach
Abstract: In this study, we present a comprehensive approach for the classification of eye diseases, specifically targeting normal, cataract, glaucoma, and diabetic retinopathy conditions. This research uses a dataset from Kaggle, which provides a wide and varied collection of retinal images to ensure good representation. The methodology encompasses advanced image processing and machine learning techniques to ensure accurate diagnosis and prediction. The preprocessing phase involves a series of image enhancement techniques …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 3, Issue 2, 2025 · pp. 15–27 Read article
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Country Cluster Visualization Based on Agricultural Imports: Unsupervised Learning Approach
Abstract: The clustering algorithm used in this analysis makes it easier for policymakers to understand performance metrics. K-means clustering has been demonstrated to be a tool for analyzing countries' agricultural imports and a visualization tool for interpreting the results in this study. Authors have used agricultural imports from 190 nations from Knoema, a web-based open data platform. Cereals, meat, and coffee imports from 190 nations in 2016 are included in the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 1–7 Read article
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Comparative Study of AI-Driven Fashion Trend Prediction System Using AI and ML: A Review
Abstract: To overcome the challenges in fashion trend forecasting, researchers have introduced several advanced and data-driven approaches. One such method uses a long short-term memory (LSTM) model combined with an encoder-decoder architecture to extract meaningful fashion content and recognize styles from product images. This model achieves higher accuracy in predicting upcoming fashion trends by incorporating varying price intervals and has shown impressive results when evaluated on the Amazon fashion dataset. Another …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 2, 2025 · pp. 35–41 Read article
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A Qualitative Study on the Lived Experiences of Women with Polycystic Ovary Syndrome Attending Gynecology OPD, Bangalore
Abstract: Polycystic ovary syndrome (PCOS) is a prevalent hormonal disorder among women of reproductive age, affecting multiple aspects of their well-being, including physical, psychological, emotional, social, and spiritual dimensions. Despite its widespread occurrence, the lived experiences of women with PCOS remain inadequately understood. The present qualitative study aimed to explore and interpret these lived experiences to enhance professional awareness, improve understanding, and promote better support for affected women. A phenomenological research …
Published in International Journal of Midwifery Nursing And Practices · Vol. 3, Issue 2, 2025 · pp. 30–40 Read article
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Role of Beowulf Clusters in Next-Generation Military Applications: A Comprehensive Study
Abstract: Beowulf clusters, which utilize cost-effective commodity hardware combined with open-source software for parallel computing, have emerged as a viable and efficient solution for high-performance computing needs. This paper explores their growing relevance and practical applications in modern and future military technologies. Contemporary military operations increasingly rely on rapid data processing, real-time intelligence, high-fidelity simulations, and autonomous decision-making systems. Beowulf clusters offer scalable and adaptable computational power that supports these demands …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 01–07 Read article
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Early Detection of Alzheimer’s Disease Using Machine Learning Techniques
Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative condition impacting a large global population. Detecting AD early is critical for timely intervention and effective management. Conventional diagnostic approaches involve cognitive assessments and neuroimaging, which are often lengthy, costly, and prone to human error. In this paper, we propose a novel approach for early detection of AD using machine learning techniques applied to multimodal data, including neuroimaging, cognitive assessments, and biomarkers. Our …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 32–43 Read article
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Transforming Rare Disease Diagnosis with AI
Abstract: Artificial intelligence is changing healthcare fast. It is making diagnoses accurate, helping doctors get better results, and streamlining how care works. This paper looks at how AI shows up in healthcare right now – where it is already making a difference, what is working, and what is still tricky. The focus is on machine learning, natural language processing, and computer vision. Particular attention is given to using AI in diagnosing …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article
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Design and Implementation of an Energy-Efficient Wireless Sensor Network for Remote Monitoring
Abstract: Applications such as industrial automation, environmental monitoring, and agriculture make extensive use of wireless sensor networks, or WSNs. Since sensor nodes frequently operate in remote locations and have limited battery power, energy efficiency is the largest problem in WSN design. This paper presents an energy-efficient WSN architecture that combines optimized clustering and duty-cycling mechanisms to reduce energy consumption and increase network lifetime. The system uses low-power ESP32 nodes with Zigbee …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 1, 2026 · pp. 22–27 Read article