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IntelCompany
distance matrixAnatomical structure
bioinformaticsMusic genre
algorithmMusic genre
Xeon Phi coprocessorCompany
OpenMPSoftware
Abstract. The paper touches upon the problem of implementation Partition Around Medoids (PAM) clustering algorithm for the Intel Many Integrated Core architecture. PAM is a form of well-known k-Medoids clustering algorithm and is applied in various subject domains, e.g. bioinformatics, text analysis, intelligent transportation systems, etc. An optimized version of PAM for the Intel Xeon Phi coprocessor is introduced where OpenMP parallelizing technology, loop vectorization, tiling technique and efficient distance matrix computation for Euclidean metric are used. Experimental results for different data sets confirm the efficiency of the proposed algorithm
The K-medoids clustering algorithm is realized by a P system in this paper. Because the membrane sys...
Given an array A of n elements and a value 2≤k≤n, a frequent item or k-majority element is an elemen...
Clustering is the task of Grouping of elements or nodes (in the case of graph) in to clusters or sub...
K-medoids clustering is categorized as partitional clustering. K-medoids offers better result when d...
K-medoids clustering is categorized as partitional clustering. K-medoids offers better result when ...
Thesis (Ph.D.)--University of Washington, 2015-12Clustering algorithms provide a way to analyze and ...
K-Medoids algorithm plays an important role in spatial data mining for its ability to eliminate the ...
Abstract. To cluster increasingly massive data sets that are common today in data and text mining, w...
Kaufman & Rousseeuw (1990) proposed a clustering algorithm Partitioning Around Medoids (PAM) which m...
The k-medoids algorithm is one of the best-known clustering algorithms. Despite this, however, it is...
R is a free statistical programming language commonly used for the analysis of high-throughput micro...
The main topic of this thesis is the implementation and subsequent optimization of high performance ...
In the area of information and technology, data is generated from a plethora of sources such as soci...
Abstract Background Partitioning around medoids (PAM) is one of the most widely used and successful ...
Clustering analysis has been a hot area of spatial data mining for several years. With the rapid dev...
The K-medoids clustering algorithm is realized by a P system in this paper. Because the membrane sys...
Given an array A of n elements and a value 2≤k≤n, a frequent item or k-majority element is an elemen...
Clustering is the task of Grouping of elements or nodes (in the case of graph) in to clusters or sub...
K-medoids clustering is categorized as partitional clustering. K-medoids offers better result when d...
K-medoids clustering is categorized as partitional clustering. K-medoids offers better result when ...
Thesis (Ph.D.)--University of Washington, 2015-12Clustering algorithms provide a way to analyze and ...
K-Medoids algorithm plays an important role in spatial data mining for its ability to eliminate the ...
Abstract. To cluster increasingly massive data sets that are common today in data and text mining, w...
Kaufman & Rousseeuw (1990) proposed a clustering algorithm Partitioning Around Medoids (PAM) which m...
The k-medoids algorithm is one of the best-known clustering algorithms. Despite this, however, it is...
R is a free statistical programming language commonly used for the analysis of high-throughput micro...
The main topic of this thesis is the implementation and subsequent optimization of high performance ...
In the area of information and technology, data is generated from a plethora of sources such as soci...
Abstract Background Partitioning around medoids (PAM) is one of the most widely used and successful ...
Clustering analysis has been a hot area of spatial data mining for several years. With the rapid dev...
The K-medoids clustering algorithm is realized by a P system in this paper. Because the membrane sys...
Given an array A of n elements and a value 2≤k≤n, a frequent item or k-majority element is an elemen...
Clustering is the task of Grouping of elements or nodes (in the case of graph) in to clusters or sub...
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