K-Means Clustering is one such technique used to provide a structure to unstructured data so that valuable information can be extracted. In this paper we are going to study the implementation of K-Means Clustering Algorithm over a distributed environment using Apache Hadoop. The main focus of ...
Heterogeneous Computing Based K-Means Clustering Using Hadoop-MapReduce FrameworkK-means is a well-known clustering algorithm in the field of data mining. It is simple to implement and its speed allows it to run on large data sets. However, it also has a drawback. Advancement in many data ...
Mini Batch K-Means algorithm is implemented by using Hadoop framework. Mini Batch K-Means is implemented using Map-Reduce programming paradigms and clusters of machine is created by using VMware virtual machine. Experimental results are compared between existing system K-Means and proposed system Mini...
In recent years, k-means has been fitted into the MapReduce framework and hence it has become a very effective solution for clustering very large datasets. However, k-means is not inherently suitable for execution in MapReduce. The iterative nature of k-means cannot be modeled in MapReduce ...
A plethora of clustering methods were developed since time unknown, but these methods have failed to prove that they are flawlessly efficient and also to give an optimized result in the field it might be that, parallel programming technique like MapReduce and evolutionary methods of computation add...
Among the diverse clustering algorithms, K-Means as a center based clustering algorithm is one of the most widely used algorithms. In this paper, we propose an efficient parallel K-mean algorithm, called MPKMeans, which utilizes the MapReduce framework for processing large volume data sets. In...
(-cl) If present, run clustering afterthe iterations have taken place--method (-xm) method The execution method to use:sequentialormapreduce.Defaultismapreduce--help (-h) Print out help--tempDir tempDir Intermediate output directory--startPhase startPhase First phase to run--endPhase endPhase ...
K-means is simple to implement, widely used clustering algorithm used for clustering. But to process large documents using one machine is good solution. So Apache Hadoop which supports Mapreduce framework can be a good solution. Hadoop uses distributed computing and distributes task to multiple ...
Parallel K-Means Clustering Based on MapReduce K-means is a pleasingly parallel algorithm that very easily fits into the Iterative map- reduce model.! 附件是一篇论文,伪代码和算法解释都很清楚。
The original K-means clustering algorithm is seriously affected by initial centroids of clustering and easy to fall into local optima. So this paper proposed an improved K-means clustering based on adaptive cuckoo search, and achieved the parallelization of the improved algorithm ...