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83 results for “K Means Clustering”
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Research ArticleOpen accessEvery word in the titleOptimized Movie Recommendation Using Hybrid Jaccard-Based K-Means Clustering Using Apache SparkShiba Prasad Dash, Rajesh Kumar Sahoo, Ram Chandra Barik et al.The need for recommendations specific to each person's interests is growing every day, making recommendation systems increasingly important in people's lives. Researchers have paid close attention to the rapid expansion of e-commerce businesses and online video streaming services like Hotstar, YouTube, and Netflix. The collaborative filtering-based RS system aims to suggest such films or videos to users based on their … …Journal of Computer Science, Vol. 22, Issue 9 · 23 Sep 2026 · 602 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAutomatic Counting for Live and Dead Cells from Trypan Blue-Stained Images by Image Analysis Based on Adaptive K-Means ClusteringSu Mon Aung, Kanyanatt Kanokwiroon, Tonghathai Phairatana, Surapong ChatpunComputer-assisted image analysis can be employed to reduce the time consumed in the routine task such as cell counting. This study aimed to establish a method to perform this routine task based on an image analysis to automatically count live and dead cells after staining with trypan blue dye. Gray scale conversion and morphological operation were applied to the input … …Journal of Computer Science, Vol. 15, Issue 2 · 1 Mar 2019 · 8,566 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA MODIFIED SPATIAL FUZZY C-MEANS CLUSTERING ALGORITHM FOR DETECTING GLAUCOMAIN RETINAL FUNDUS IMAGESS. J. Grace Shoba, A. Brintha ThereseGlaucoma is a disease which affects the eye and causes blindness. It is an ophthalmologist disease characterized by an increase in Intraocular Pressure (IOP). The glaucoma usually affects the optic disc on the retina which increases the cup size. There are various parameters to identify and diagnose glaucoma. The clustering technique is introduced to detect the glaucoma from the optic … …Journal of Computer Science, Vol. 10, Issue 8 · 5 Mar 2014 · 7,027 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleCOMPARATIVE STUDY OF K-MEANS AND K-MEANS++ CLUSTERING ALGORITHMS ON CRIME DOMAINBashar Aubaidan, Masnizah Mohd, Mohammed AlbaredThis study presents the results of an experimental study of two document clustering techniques which are k-means and k-means++. In particular, we compare the two main approaches in crime document clustering. The drawback of k-means is that the user needs to define the centroid point. This becomes more critical when dealing with document clustering because each center point represented by … …Journal of Computer Science, Vol. 10, Issue 7 · 19 Feb 2014 · 7,762 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleFAULT DETECTION IN SWITCHED RELUCTANCE MOTOR DRIVES USING DISCRETE WAVELET TRANSFORM AND K-MEANS CLUSTERINGV. S. Chandrika, A. Ebenezer JeyakumarThis study presents a novel method of detection of inter turn shorts based on k means clustering technique. In addition to inter turn short detection, the other faults like open, short, phase to phase faults and DC volt-age faults are detected through wavelet transforms and k means clustering. Open and short faults are classified using artificial neural network. All other … …American Journal of Applied Sciences, Vol. 11, Issue 3 · 30 Dec 2013 · 7,983 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleContent Based Medical Image Retrieval with Texture Content Using Gray Level Co-occurrence Matrix and K-Means Clustering AlgorithmsB. Ramamurthy, K. R. ChandranProblem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies … …Journal of Computer Science, Vol. 8, Issue 7 · 24 May 2012 · 7,448 accessesView PDFCite
Research ArticleOpen accessEvery word in the titlePredicting Missing Attribute Values Using k-Means ClusteringNambiraj Suguna, Keppana Gowder ThanushkodiProblem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,873 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComputational Complexity between K-Means and K-Medoids Clustering Algorithms for Normal and Uniform Distributions of Data PointsT. Velmurugan, T. SanthanamProblem statement: Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging to different groups are dissimilar. Clustering is an unsupervised learning technique. The main advantage of clustering … …Journal of Computer Science, Vol. 6, Issue 3 · 31 Mar 2010 · 19,434 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleNew Efficient Strategy to Accelerate k-Means Clustering AlgorithmMoh’d Belal Al- Zoubi, Amjad Hudaib, Ammar Huneiti, Bassam HammoOne of the most popular clustering techniques is the k-means clustering algorithm. However, the utilization of the k-means is severely limited by its high computational complexity. In this study, we propose a new strategy to accelerate the k-means clustering algorithm through the Partial Distance (PD) logic. The proposed strategy avoids many unnecessary distance calculations by applying efficient PD strategy. Experiments … …American Journal of Applied Sciences, Vol. 5, Issue 9 · 30 Sep 2008 · 7,437 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSegmentation of Exudates via Color-based K-means Clustering and Statistical-based ThresholdingAtef Zaki Ghalwash, Aliaa Abdel-Halim Youssif, Ali Mohamed Nabil AllamThis paper provides a novel approach for the problem of detecting the yellowish lesions in the eye fundus images, such as hard and soft exudates, in a fully-automated manner. To solve this problem of segmenting exudates automatically, the fundus image was first converted into the L*a*b* color space to decouple the chromaticity information of the image. Next, the fundus image … …Journal of Computer Science, Vol. 13, Issue 10 · 14 Oct 2017 · 7,725 accessesView PDFCite