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262 articles for “Clustering”
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Research ArticleOpen accessEvery word in the titleDETECTING MULTIPLE INTRUSION ATTACKS USING PERMANENT GIRTH CLUSTERING MODEL IN WIRELESS SENSOR NETWORKG. Jayamurugan, P. KamalakkannanSecurity is the central challenge and one of the serious concerns for designing reliable sensor networks. Of the different types of security threats in wireless sensor network, particularly dangerous attack is the replica node attack, in which the opponent takes the secret keying materials from a compromised node. It then produces large number of attacker-controlled replicas that divide up the … …Journal of Computer Science, Vol. 10, Issue 8 · 1 Apr 2014 · 6,467 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleCOMBINATION OF DIFFERENCE SUBSPACE AND OPPURTUNISTIC CLUSTERING ON HIGH DIMENSIONAL DATAM. Ravichandran, A. ShanmugamClustering is recognized as sigificant technique for analysing data and concentric effort has been taken in different domains comprises of recognition of pattern, statistical analysis and data mining for decades. Subspace clustering is developed from the group of cluster objects from all subspaces of a dataset. During clustering of objects involing higher dimension, the accuracy and effectiveness of traditional clustering … …Journal of Computer Science, Vol. 10, Issue 8 · 11 Mar 2014 · 6,355 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 title3-DIMENSIONAL EAR RECOGNITION BASED ITERATIVE CLOSEST POINT WITH STOCHASTIC CLUSTERING MATCHINGKhamiss Masaoud S. Algabary, Khairuddin Omar, Md Jan NordinEar recognition is a new technology and future trend for personal identification. However, the false detection rate and matching recognition are very challenging due to the ear complex geometry. The Scope of the study is to introduced a combination of Iterative Closest Point (ICP) and Stochastic Clustering Matching (SCM) algorithm for 3D ears matching based on biometrics field with a … …Journal of Computer Science, Vol. 10, Issue 3 · 29 Nov 2013 · 6,812 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleTWO PHASE CLUSTERING METHOD FOR LEACH PROTOCOL FOR EFFECTIVE CLUSTER HEAD SELECTIONRathi, ViswanathanA Wireless Sensor Networks (WSN) comprise of a huge quantity of clusters of sensor nodes that can inter communicate with all nodes and with the base station through a wireless link. All clusters are corresponding to a cluster head to provide a direct communication to the base station and in addition with other cluster heads. Obviously well-organized selection of cluster … …Journal of Computer Science, Vol. 10, Issue 3 · 21 Nov 2013 · 7,122 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAdvanced Energy Efficient Passive Clustering Mobility in Wireless Sensor NetworksAouad Siham, Maach Abdelillah, El Ganami DrissWireless sensor networks have a wide applicability and increase deployment in the near future. In most of those applications, the network is composed of an important number of nodes deployed in an extensive area in which not all nodes are directly connected and based on clustering techniques. The Clustering can considerably reduce transmission overhead during flooding. In fact, by using … …American Journal of Applied Sciences, Vol. 10, Issue 12 · 24 Oct 2013 · 6,756 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA NOVEL APPROACH BASED ON GENETIC FUZZY CLUSTERING AND ADAPTIVE NEURAL NETWORKS FOR SALES FORECASTINGAttariuas Hichama, Bouhorma Mohameda, El Fallahi AbdellahbThis article proposes a new hybrid sales forecasting system based on genetic fuzzy clustering and Back-Propagation (BP) Neural Networks with adaptive learning rate (GFCBPN).The proposed architecture consists of three stages: (1) utilizing Winter’s Exponential Smoothing method and Fuzzy C-Means clustering, all normalized data records will be categorized into k clusters; (2) using an adapted Genetic Fuzzy System (MCGFS), the fuzzy … …Journal of Computer Science, Vol. 9, Issue 8 · 2 Jul 2013 · 6,669 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleHybrid Ant-Based Clustering Algorithm with Cluster Analysis TechniquesWafa’a Omar, Amr Badr, Abd El-Fattah HegazyCluster analysis is a data mining technology designed to derive a good understanding of data to solve clustering problems by extracting useful information from a large volume of mixed data elements. Recently, researchers have aimed to derive clustering algorithms from nature’s swarm behaviors. Ant-based clustering is an approach inspired by the natural clustering and sorting behavior of ant colonies. In … …Journal of Computer Science, Vol. 9, Issue 6 · 7 Jun 2013 · 7,306 accessesView PDFCite