[PDF] Top 20 Study on K-Means Clustering using MapR in Hadoop
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Study on K-Means Clustering using MapR in Hadoop
... The K-implies grouping calculation is a famous unsupervised bunching procedure used to recognize similitude’s between articles dependent on separation vectors fit to little ...customary K-implies bunching ... See full document
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Optimised Parallel K-Means Clustering using YARN in Hadoop
... Apache Hadoop and the clustering technique, this work has the hypothesis that it is possible to find a better performance when processing large datasets for the K-Means and K-Medoids ... See full document
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Parallel Implementation of Fuzzy Clustering Algorithm Based on MapReduce Computing Model of Hadoop –A Detailed Survey
... and clustering algorithms based on cloud computing platform gradually becomes a hot topic of ...following: Study general law of the parallelization of clustering ...parallel clustering ... See full document
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Implementation of K Means Clustering Algorithm in Hadoop Framework
... the K-Means clustering algorithm in MapReduce is to state and handle the input and output of the ...to K- Means algorithm must be prepared as key/value ...hand, clustering of ... See full document
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Review on Optimised Parallel K-Means Clustering using YARN in Hadoop
... Apache Hadoop YARN : Apache Hadoop YARN is the resource management and job scheduling technology in the open source Hadoop distributed processing ...a Hadoop cluster and scheduling tasks to be ... See full document
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Review on Document Clustering Using K-Means over Hadoop
... anticipate, or group approaching information by deciding the probability of the new, inconspicuous information having a place with a class learned in the preparation stage. Classifiers can be utilized as a part of BCI ... See full document
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A Novel Clustering Algorithm Using K means (CUK)
... from study conducted on the survival of patients who had undergone surgery for breast ...and K-means, after running them for 100 times we take the average ... See full document
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Comparative Study of Clustering Algorithms: Filtered Clustering and K-Means Cluttering Algorithm Using WEKA
... ABSTRACT: Clustering is an unsupervised learning problem which is used to determine the intrinsic grouping in a set of unlabeled data. Grouping of objects is done on the principle of maximizing the intra-class ... See full document
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Medical Image Segmentation using Modified K Means Clustering
... the study of anatomical structures and to identify the region of ...modified k means clustering is ...C-Means Clustering, K-Means Clustering with Modified ... See full document
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Document Clustering Using Enhanced Tw-K-Means
... techniques used. This makes the user to choose one among the several available text mining techniques. In (Yannis Haralambous & Philippe Lenca, 2014), the authors use the concept of text item pruning and text ... See full document
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Document Clustering Using K-Means videHadoop
... called HADOOP, using the solution provided by ...computers using simple programming models. The Hadoop framework application works in an environment that provides distributed storage and ... See full document
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Image Segmentation using K means clustering and Thresholding
... comparative study of two segmentation techniques has been performed in this ...The K-means clustering and thresholding techniques were chosen for ...segmentation. Using these two ... See full document
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Clustering large datasets using K-means modified inter and intra clustering (KM-I2C) in Hadoop
... time. Hadoop can compute map and reduce jobs in parallel to cluster large datasets effec- tively and ...standard K-means method is the most popular clustering method due to its simplicity and ... See full document
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Hybrid optimization for k-means clustering learning enhancement
... 2012). Clustering is a technique of a great importance, which is applied to several fields such as information retrieval and knowledge ...discovery. Using this technique, scholars are capable of finding ... See full document
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CLASSIFICATION BY K MEANS CLUSTERING
... (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each ...re-calculate k new centroids as barycenters of the clusters resulting from the previous ...these k ... See full document
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Clustering Performance Comparison using K-means Clustering Algorithm and IPCA
... K-Means Clustering: - the thought behind the k-means formula is that every of k clusters may be described by the mean of the documents allotted thereto cluster, that is named ... See full document
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An efficient document clustering by using adaptive k-means clustering algorithm
... fast clustering-based feature subset selection by initially separating the features into ...adaptive K-Means algorithm whereEuclidean and Cosine distance measures are employed for finding the ... See full document
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ADMISSION MANAGEMENT USING RELATIONAL K-MEANS CLUSTERING
... that k-means clustering algorithm is simplest algorithm as compared to other ...the clustering algorithm is having many disadvantages related to either assumption of the values or its ... See full document
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Colour Constancy using K means Clustering Algorithm
... Image colour constancy quality assessment methods are divided into two categories called objective and subjective methods. Angular error and Euclidean distance are the main two commonly used methods to quantify ... See full document
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Optimization Of K Means Clustering For DECT Using ACO
... [1] Sadhana Tiwari and Tanu Solanki, “An Optimized Approach for k-means Clustering.” International Journal of Computer Applications (0975 – 8887) 9th International ICST Conference on Heterogeneous ... See full document
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