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Research ArticleOpen accessEvery word in the titleK-MEANS BASED IMAGE DENOISING USING BILATERAL FILTERING AND TOTAL VARIATIONDanu Wiroteurairuang, Sanun Srisuk, Chom Kimpan, Thanwa SripramongBilateral filter and Total variation image denoising are widely used in image denoising. In low noisy level, bilateral filtering is better than TV denoising for it reveals better SNR and sharper edges. However, in high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. It is very difficult to perform denoising of … …Journal of Computer Science, Vol. 10, Issue 12 · 30 Dec 2014 · 8,171 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 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 titleThe use of Random Forest Classification and K-means Clustering Algorithm for Detecting Time Stamped Signatures in the Active NetworksKamalanaban Ethala, R. Shesadri, N. G. RenganathanIn day to day information security infrastructure, intrusion detection is indispensible. Signature based intrusion detection system mechanisms are often available in detecting many types of attacks. But this mechanism alone is not sufficient in many cases. Another intrusion detection method viz K-means is employed for clustering and classifying the unlabelled data. IDS is a special embedded device or relied software … …Journal of Computer Science, Vol. 9, Issue 7 · 18 Jun 2013 · 7,496 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 titleMedical Image Compression Using Fuzzy C-Means Based Contourlet TransformM. Tamilarasi, V. PalanisamyProblem statement: To meet the demand for high speed transmission of image in efficient image storage and remote medical treatment, the efficient image compression is essential. The contourlet transform along with wavelet theory has great potential in medical image compression. Approach: The significant portion of the medical image applied with Fuzzy C-means based contourlet transform. DWT applied to the rest … …Journal of Computer Science, Vol. 7, Issue 9 · 25 Jul 2011 · 7,082 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 titleSingle Pass Seed Selection Algorithm for k-MeansK. Karteeka Pavan, Allam Appa Rao, A.V. Dattatreya Rao, G. R. SridharProblem statement: The k-means method is one of the most widely used clustering techniques for various applications. However, the k-means often converges to local optimum and the result depends on the initial seeds. Inappropriate choice of initial seeds may yield poor results. k-means++ is a way of initializing k-means by choosing initial seeds with specific probabilities. Due to the random … …Journal of Computer Science, Vol. 6, Issue 1 · 31 Jan 2010 · 7,541 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