[PDF] Top 20 Firefly Algorithm based Map Reduce for Large Scale Data Clustering
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Firefly Algorithm based Map Reduce for Large Scale Data Clustering
... K-Means algorithm is always especially ...FF algorithm mitigates the time and costs in a massive clustering procedure in ...distributed algorithm and it is even more suitable to analyse a ... See full document
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Using Map-Reduce for Large Scale Analysis of Graph-Based Data
... use Map-Reduce model to solve graph problems, and give out constructive suggestions on graph-based algorithm design on Map-Reduce model and lots of algorithms can be represented ... See full document
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A Firefly based improved clustering algorithm
... automated data analysis techniques are becomes more crucial now in these days, because a huge amount of records in unstructured format is generated every ...automatic data analysis data mining ... See full document
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Map Reduce Algorithm based Fast Detection of Connected Components in Large Scale Graph Processing
... and data frameworks, size of genuine graphs has expanded to billions of hubs and ...substantial scale graphs swung to be a computationally difficult ...substantial scale information handling ...huge ... See full document
6
Map/Reduce Affinity Propagation Clustering Algorithm
... Big Data issue. Big Data is a collection of data set so large that it becomes difficult to ...analyzing data is cluster ...a clustering algorithm requires no pre- set ... See full document
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Map Reduce Design for Data Clustering
... and map reduce programming model. Since HDFS uses data replication concept, the loss of data is ...managing large amount of data, data access becomes ...KMeans ... See full document
5
An Online Based Approach for Health Care Using Clustering Algorithm with Map Reduce For Scalable Data
... Abstract: The world is moving on the speed of the internet. With the increase of internet user’s in past years has given rise to multiple online things like booking tickets, shopping. Shopping through internet has paved ... See full document
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Big Data Analytics: Map Reduce Function using BIRCH Clustering Algorithm
... Big Data information is represented in unstructured form and NoSQL is used for query ...of data also too large and simple Query processing is not sufficient and ...that large volume of ... See full document
8
Enhancing Map-Reduce Mechanism for Big Data with Density-Based Clustering
... distributed data mining for the big ...the large dataset across cluster of computers using single programming ...machine’s data can be mine in the scalable ... See full document
6
Combined Inference Approach for Large Scale Ontologies based on Map Reduce Paradigm
... Among the analyses, we have a tendency to ruin down the page assess motion files stories of Wikipedia. we have a tendency to acquire a bit field containing four segments. We have a tendency to set project code and pages ... See full document
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Overview of Fuzzy Clustering Algorithm on Hadoop Map Reduce Computing
... Big Data is a one vital component in today’s era. It deals with large amount of heterogeneous data in every ...the data using traditional data processing applications. Big Data ... See full document
6
Large-Scale Data Sets Clustering Based on MapReduce and Hadoop
... for data—intensive distributed parallel ...important data mining ...in large-scale data sets clustering on the single computer, we design and realize a parallel K- Means ... See full document
8
Map Reduce clustering in Incremental Big Data processing
... FP algorithm. Though retrieval of statistics becomes easier with map reduce, the interdependence of map and decrease responsibilities requires extra fault ...array based languages, like ... See full document
7
Schema Matching for Large-Scale Data Based on Ontology Clustering Method
... interactive clustering method has been proposed based on hierarchical clustering for matching web ...to reduce the search ...merging clustering approach for holistic schema matching ... See full document
8
Map-Reduce Algorithm for Mining Outliers in the Large Data Sets using Twister Programming Model
... the data: A top-k outlier is an object having at most the k th largest sum of squared values in a fixed number of the lowest order principal components, where each component is normalized to its ...the data ... See full document
6
Efficient Clustering on Big Data Map Reduce Using DBScan
... thickness based information clus-tering calculation that is generally utilized because of its capacity to discover self-assertively formed groups in loud ...to scale which restrains its utility when working ... See full document
6
Network Hot Topic Discovery of Fuzzy Clustering Based on Improved Firefly Algorithm
... mutations, data sparsity and other issues, which lead to the problem of accuracy in topic ...discovery. Based on this, fuzzy clustering is ...for clustering hot topics, but the cluster has the ... See full document
14
Optimization and analysis of large scale data sorting algorithm based on Hadoop
... module reduce to send out map into reduce in partition ...in reduce, we know what the corresponding divided data segment is and the corresponding data segments are; Obtain the ... See full document
11
NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map
... central data in each cluster is considered as the new centroid and each data is assigned to the nearest ...this algorithm match the K-means ...(FCM) algorithm [6], [7] puts forward a concept ... See full document
10
An Efficient Clustering Algorithm for Market Basket Data Based on Small Large Ratios
... of clustering market-basket data is “We are given a database of transactions, a minimum support, and a maximum ...a clustering U such that the total cost is ...of clustering algorithm ... See full document
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