The aim of this work is the parallel implementation of k-means in MATLAB, in order to reduce the execution time. Specifically, a new function in MATLAB for serial k-means algorithm is developed, which meets all the requirements for the conversion to a function in MATLAB with parallel computations. Additionally, two different variants for the definition of initial values are presented. In the sequel, the parallel approach is presented. Finally, the performance tests for the computation times respect to the numbers of features and classes are illustrated
A cluster is a collection of data objects that are similar to each other and dissimilar to the data ...
The K-means algorithm is one of the more known unsupervised algorithms that aims to partition data p...
4th IEEE International Congress on Big Data, BigData Congress ( 2015 : New York City; United States...
The aim of this work is the parallel implementation of k-means in MATLAB, in order to reduce the exe...
The $K$-means algorithm is undoubtedly one of the most popular clustering analysis techniques, due t...
The ready availability of vast quantities of data has driven the need for data mining algorithms tha...
Handling and processing of larger volume of data requires efficient data mining algorithms. k-means ...
K-means algorithm is one of the unsupervised learning clustering algorithm that can be used to solve...
International audiencek-means is a standard algorithm for clustering data. It constitutes generally ...
The K-Means algorithm is one the most efficient and widely used algorithms for clustering data. Howe...
International audienceIn this paper we propose a MapReduce implementation of G-means, a variant of k...
The k-means algorithm is a widely used clustering tech-nique. Here we will examine the performance o...
Clustering is defined as the grouping of similar items in a set, and is an important process within ...
K-means is a well-known clustering algorithm often used for its simplicity and potential efficiency....
Processing power of pattern classification algorithms on conventional platforms has not been able to...
A cluster is a collection of data objects that are similar to each other and dissimilar to the data ...
The K-means algorithm is one of the more known unsupervised algorithms that aims to partition data p...
4th IEEE International Congress on Big Data, BigData Congress ( 2015 : New York City; United States...
The aim of this work is the parallel implementation of k-means in MATLAB, in order to reduce the exe...
The $K$-means algorithm is undoubtedly one of the most popular clustering analysis techniques, due t...
The ready availability of vast quantities of data has driven the need for data mining algorithms tha...
Handling and processing of larger volume of data requires efficient data mining algorithms. k-means ...
K-means algorithm is one of the unsupervised learning clustering algorithm that can be used to solve...
International audiencek-means is a standard algorithm for clustering data. It constitutes generally ...
The K-Means algorithm is one the most efficient and widely used algorithms for clustering data. Howe...
International audienceIn this paper we propose a MapReduce implementation of G-means, a variant of k...
The k-means algorithm is a widely used clustering tech-nique. Here we will examine the performance o...
Clustering is defined as the grouping of similar items in a set, and is an important process within ...
K-means is a well-known clustering algorithm often used for its simplicity and potential efficiency....
Processing power of pattern classification algorithms on conventional platforms has not been able to...
A cluster is a collection of data objects that are similar to each other and dissimilar to the data ...
The K-means algorithm is one of the more known unsupervised algorithms that aims to partition data p...
4th IEEE International Congress on Big Data, BigData Congress ( 2015 : New York City; United States...