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141 results for “cluster data”
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Research ArticleOpen accessIn the abstractFractional Lion Algorithm-An Optimization Algorithm for Data ClusteringSatish Chander, P. Vijaya, Praveen DhyaniClustering divides the data available as bulk into meaningful, useful groups (Clusters) without any prior knowledge about the data. Cluster analysis provides an abstraction from individual data objects to the clusters in which those objects reside. It is a key technique in the data mining and has become an important issue in many fields. This paper presents a novel Fractional … …Journal of Computer Science, Vol. 12, Issue 7 · 12 Aug 2016 · 7,673 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdaptive Classification Method Based on Data DecompositionAyman E. Khedr, Amira M. Idrees, Ahmed I. El SeddawyKnowledge discovery is one of the vital fields which strongly supports decision making by applying different techniques based on the targeted field and the required information. Focusing on clustering and classification techniques, this paper presents an approach for adapting one of the classification algorithms for supporting decision making procedure in radiology data analysis field. The proposed adaptation is based on … …Journal of Computer Science, Vol. 12, Issue 1 · 20 Feb 2016 · 7,423 accessesView PDFCite
Research ArticleOpen accessIn the abstractGenetic Relationship among Wild Medicinal Genotypes of Ziziphora canescens Benth. and Ziziphora tenuior L. and Detection of Genetic Variations Resulted From Tissue Culture, Salinity and pH MediaAbdulkarim Dakah, Mohamad Suleiman, Salim ZaidZiziphora tenuior L. and Ziziphora canescens Benth. are two of plants which using in folk medicine in the Kalamoon Mountains areas of Syria for cough, stomachache and dysentery. Samples of Ziziphora genotypes were collected from four different locations (Assal Al-Ward, Yabroud, Qarah and Maaraba), 20 RAPD and 12 ISSR primers were used to assess the genetic diversity of 10 genotypes. … …American Journal of Agricultural and Biological Sciences, Vol. 10, Issue 3 · 19 Oct 2015 · 7,897 accessesView PDFCite
EditorialOpen accessIn the abstractData Mining in Time Series: Current Study and Future TrendTan Yan, Liudmila Ulanova, Ye Ouyang, Fengyuan XuTime series represent sequences of data points where usually their order is defined by the time when they were recorded. Thus, virtually any sequential recordings can be stored as time series: Stock prices, weather conditions, physical system parameters change, product quality monitoring, etc. This leads to ubiquity of time series in all scientific and practical fields (Esling and Agon, 2012); … …Journal of Computer Science, Vol. 10, Issue 12 · 26 Jan 2015 · 8,756 accessesView PDFCite
Research ArticleOpen accessIn the abstractSENSOR NETWORK PERIODICAL DATA ASSEMBLY USING IMBALANCED CLUSTERING ENERGY EFFICIENT MECHANISMK. Munusamy, R. M.S. ParvathiFast technical advances and development in wireless communication resulted in the huge consumption of wireless sensor networks. The nodes in these networks are ready to be sensed, processing the data and transmitting radio units. While several research works have been conducted related to the power-aware and energy-efficiency using Cooperative Networking protocol (CONET) that reforms clusters but the multiple rate remains … …Journal of Computer Science, Vol. 10, Issue 10 · 28 Jun 2014 · 6,648 accessesView PDFCite
Research ArticleOpen accessIn the abstractSIGNED GRAPH APPROACH IN ADAPTIVE TRANSMISSION POWER TO ENHANCE THE LIFETIME OF WIRELESS SENSOR NETWORKSA. Babu Karuppiah, S. RajaramA Wireless Sensor Network (WSN) comprises a collection of sensor nodes networked for applications like surveillance, battlefield, monitoring of habitat. Nodes in a WSN are usually highly energy-constrained and expected to operate for long periods from limited on-board energy reserves. When a node transmits data to a destination node the data is overheard by the nodes that are in the … …American Journal of Applied Sciences, Vol. 11, Issue 8 · 13 May 2014 · 6,729 accessesView PDFCite
Research ArticleOpen accessIn the abstractBRAIN TUMOR CLASSIFICATION BASED ON CLUSTERED DISCRETE COSINE TRANSFORM IN COMPRESSED DOMAINV. Anitha, S. MurugavalliThis study presents a novel method to classify the brain tumors by means of efficient and integrated methods so as to increase the classification accuracy. In conventional systems, the problem being the same to extract the feature sets from the database and classify tumors based on the features sets. The main idea in plethora of earlier researches related to any … …Journal of Computer Science, Vol. 10, Issue 10 · 6 May 2014 · 7,532 accessesView PDFCite
Research ArticleOpen accessIn the abstractGENETIC DIVERSITY OF OKRA (ABELMOSCHUS ESCULENTUS L.) GENOTYPES FROM DIFFERENT AGRO-ECOLOGICAL REGIONS REVEALED BY AMPLIFIED FRAGMENT LENGTH POLYMORPHISM ANALYSISNaser M. SalamehThis study was carried out to assess the genetic diversity in 48 genotypes and accessions using eight Amplified Fragment Length Polymorphism (AFLP) primer-pairs. The eight selected AFLP primer-pairs generated a total of 150 polymorphic loci. Using the generated AFLP data, the Un-Weighted Pair Group Method with Arithmetic Average (UPGMA) ordered the genotypes into six groups based on Dice similarity coefficient. … …American Journal of Applied Sciences, Vol. 11, Issue 7 · 29 Apr 2014 · 8,069 accessesView PDFCite
Research ArticleOpen accessIn the abstractA 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 accessIn the abstractCOMPARATIVE 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