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128 articles for “clustering algorithms”
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Fuzzy Based Clustering Algorithm Rebroadcast with Ant Colony Based Cluster Head Selection for Minimizing the Routing Overhead in Mobile Ad-Hoc Networks
Abstract: Mobile Ad-hoc Networks (MANET) are built with no sort of management, rendering the quality of servicedespite the routing overhead. With MANET, mobile hosts cannot interact with other hosts in a single hop.In the case of multi-hop, packets transmitted by the source host get relayed with the help of multipleintermediate hosts prior to its arrival at the destination host. Owing to the node movement in MANETs,path outages and route discoveries happen …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 8, Issue 2, 2021 · pp. 12–27 Read article
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Colour Based Segmentation of a Landsat Image Using K-Means Clustering Algorithm
Abstract: AbstractImage segmentation is one of the typical vintage subjects in image processing and it acts as a bulls-eye of the image processing technique. By definition, image segmentation means identifying the similar regions in the image; or in other words, identifying the homogenous pixels in the image and grouping all these pixels based on the homogeneity condition considered. This homogeneity condition can be like, colour, texture, size, compactness etc. It is …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 4, Issue 3, 2017 · pp. 31–38 Read article
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A PCA Based K-Means Clustering Algorithm for Wireless Sensor Nodes
Abstract: This paper presents a novel and improved approach for K-Means Clustering of wireless sensor networks, by using Principal Component Analysis for data reduction on the raw data. A wireless sensor network consisting of 100 nodes is classified into three different clusters using PCA based K-Means Algorithm. Davies-Bouldin Index is used as a parameter to check the effectiveness of the clustering algorithm. Experimental results demonstrate that the PCA based K-Means Algorithm …
Published in Journal of Web Engineering & Technology · Vol. 2, Issue 2, 2015 · pp. 6–10 Read article
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CTCHFA: Discovery of Circulating Tumor Cells in Metastatic Breast Cancer and Nonmetastatic Cancer by using Novel Hybrid Hierarchical Clustering Algorithm in Firefly Distance
Abstract: AbstractBlood testing for circulating tumor cells (CTCs) has emerged as one of the highest fields in cancer research. CTC detection are an early gene marker of reaction to systemic therapy, whereas their molecular characterization has a strong field that can be translated to individualized targeted treatments and spare breast cancer (BC) patients from unnecessary and ineffective therapies. Genomic research regarding CTCs monitoring for BC is limited due to the lack …
Published in Journal of Computer Technology & Applications · Vol. 6, Issue 1, 2015 · pp. 9–18 Read article
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A Survey on Different Clustering Algorithms with Their Major Features
Abstract: Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. Clustering is used to organize data for efficient retrieval. The aim is to create homogeneous subgroups of examples. The individuals in the same subgroup are similar; the individuals in different subgroups are as different as possible. One of the problems in clustering is the identification of clusters in given data. A popular technique …
Published in Journal of Web Engineering & Technology · Vol. 1, Issue 3, 2014 · pp. 19–24 Read article
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Superpixel Segmentation using DBSCAN Clustering Algorithm
Abstract: AbstractSuperpixel division strategies are generally utilized as a pre-preparing venture to accelerate picture handling tasks. They assemble the pixels of a picture into homogeneous locales while attempting to regard existing shapes. In this paper, we propose a quick superpixels division calculation with contour adherence utilizing the density based spatial clustering of applications with noise (DBSCAN) algorithm. We implement a two-stage processing to decrease the computational costs of superpixel algorithms. First …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 4, Issue 3, 2017 · pp. 26–36 Read article
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Implementation of K-Means clustering algorithm with technical indicators to identify Profitable stocks
Abstract: In the ever-changing world of stock market trading, accurately predicting price movements is key to maximizing profits. Technical analysis, which looks at past price data to predict future trends, provides valuable insights for investors. This paper delves into using machine learning methods, particularly the K-Means clustering algorithm, along with moving average data, to categorize daily trading patterns. By breaking down the market into clusters and examining the main patterns within …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 8–14 Read article
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An Improved K-means Clustering Algorithm for Classification of Odor/Gas Sensor Data Using Normalized Cosine Distance Parameter
Abstract: This paper presents a novel approach of K-means Clustering for classification of odors/gases (E-nose) using cosine distance as a distance parameter. A sensor array constituting five sensors is exposed to four different types of gases to extract data. The problem of classifying the data into respective classes in considered as a K-means Clustering task. To quantify the amount of similitude between the data corresponding to same classes; usually euclidean distance …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 2, Issue 2, 2015 · pp. 56–60 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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Image Segmentation by Grouping Pixels in Color and Image Space Simultaneously using DBSCAN Clustering Algorithm
Abstract: In this paper, the authors propose a technique for image segmentation by integrating the spatial connectivity and color features of pixels. Taking into account that an image can be considered as a dataset in which each pixel has a spatial location and a color value, color image segmentation can be obtained by clustering these pixels into different groups of coherent spatial connectivity and color. To discover clusters in spatial databases …
Published in Journal of Remote Sensing & GIS · Vol. 4, Issue 3, 2013 · pp. 52–60 Read article
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An Improved Cluster Head Election Algorithm Using Energy or Weight of Path in MANET
Abstract: Mobile ad hoc network is one of the well-known technologies among wireless networks. Due to mobility and dynamic topology the network suffers from different security and performances issue. Therefore, the clustering algorithms are implemented for improving the scalability and performance of the routing algorithms. The presented work demonstrates a MANET clustering algorithm and their design strategy. The overview of the proposed routing techniques is discussed in this paper.Cite this ArticleKuldeep …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 3, Issue 1, 2016 · pp. 10–16 Read article
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Biological Network Clustering using Distance and Graphical Network Algorithm
Abstract: Clustering is a process of grouping together similar data items together based on certain percentage of similarity or certain amount of threshold value. The study of the protein-protein interaction is an important task as this is necessary in order to identify how the proteins are grouped together and how they interact with each other. This process of studying or identifying the protein-protein interaction so far done anciently is followed by …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 5, Issue 1, 2015 · pp. 6–14 Read article
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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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A Review on Algorithms of Cluster Head in Mobile Ad Hoc Networks
Abstract: AbstractMobile ad hoc networks consists of mobile nodes that communicates with each different with none mounted topology. Cluster head choice, energy consumption and security may be a great challenge for MANET recently. Optimizing cluster head choice minimizes the energy consumption during a specific network and in addition reduces the information transmission overheads. There are several algorithms for cluster head choice that are supported many parameters. For higher utilization of energy, …
Published in Current Trends in Signal Processing · Vol. 8, Issue 3, 2018 · pp. 1–6 Read article
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A Survey Paper on Gene Expression Data in Data Mining
Abstract: For analyzing the gene expression data, data mining technique, i.e. data clustering methods are to be more victorious. Cluster analyses are performed to look for separating the given data set into groups that are based on particular characteristics or behavior;therefore the data are much similar in the same group and different from other cluster/groups. From past few decades, various studies on cluster analysis have been developed. Most traditional clustering algorithms …
Published in Journal of Computer Technology & Applications · Vol. 8, Issue 1, 2017 · pp. 14–22 Read article
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A New Variant of Bat Algorithm and Clustering Approach for Optimization Problems
Abstract: AbstractThis paper proposes a new variant of bat algorithm for optimization problems for solving global and continuous optimization problems to expand the weakness of BAT. The planned algorithm uses the genetic bat tactics for improving the search and to overcome the deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. The outcomes are taken on six benchmark optimization problems. From upshots, it …
Published in Journal of Web Engineering & Technology · Vol. 4, Issue 3, 2017 · pp. 1–5 Read article
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A Novel Sentence Ranking Clustering Based Algorithm for Text Summarization
Abstract: Text Summarization is the process of generating reduced text from source document by extraction or generation. A novel approach will be presented for extractive text summarization. The proposed technique performs filtering and then ranking on sentences based on NLP text mining techniques. This method uses the synonyms extracted from online thesaurus dictionary to identify similar sentences and then clusters these sentences to summarize. The sentences are ranked based on sentence …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 2, Issue 2, 2015 · pp. 1–6 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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Detection of Cancer using Machine Learning Algorithms
Abstract: Cancer is a group of diseases characterized by uncontrolled growth and spread of abnormal cells. There are over 100 types of cancer. And any part of the body can be affected. Cancer has become 2nd leading cause of death. Some hospitals offer cancer screening tests; the test results need to be evaluated by an oncologist. The cancer screening test are very expensive and not available in all of the hospital. …
Published in Trends in Machine design · Vol. 7, Issue 3, 2020 · pp. 9–16 Read article
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A New Method to Optimize Initial Cluster Number and Then Apply K-Means Algorithm to Perform Image Segmentation
Abstract: Clustering is described as the task of merely grouping a set of data points, which in our case are represented by image pixels, with the application of one or more algorithms, into super-pixels. The k-means clustering algorithm is used for many day-to-day practical applications where the quality of the obtained clusters depends heavily on the initial selection of centroids. In literature, several methods have already been proposed for the improvement …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 5, Issue 2, 2018 · pp. 18–24 Read article