Efficient algorithm for big data clustering on single machine

Big data analysis requires the presence of large computing powers, which is not always feasible. And so, it became necessary to develop new clustering algorithms capable of such data processing. This study proposes a new parallel clustering algorithm based on the k-means algorithm. It significantly...

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Veröffentlicht in:CAAI Transactions on Intelligence Technology Jg. 5; H. 1; S. 9 - 14
Hauptverfasser: Alguliyev, Rasim M, Aliguliyev, Ramiz M, Sukhostat, Lyudmila V
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Beijing The Institution of Engineering and Technology 01.03.2020
John Wiley & Sons, Inc
Wiley
Schlagworte:
Accelerometers
Algorithms
Big Data
big data analysis
big data clustering
C6130 Data handling techniques
Censuses
Centroids
cluster centroids
Clustering
clustering algorithms
clustering speed
computing powers
Data analysis
Data points
Data processing
Datasets
Efficiency
Experiments
initial dataset
k-means algorithm
Massive data points
pattern clustering
Performance evaluation
Personal computers
Research Article
single machine
Standard deviation
United States > US
data analysis
computing powers
single machine
big data clustering
data processing
Big Data
data points
cluster centroids
initial dataset
pattern clustering
clustering algorithms
clustering speed
k-means algorithm
big data analysis
ISSN:2468-2322, 2468-6557, 2468-2322
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Beschreibung
Zusammenfassung:Big data analysis requires the presence of large computing powers, which is not always feasible. And so, it became necessary to develop new clustering algorithms capable of such data processing. This study proposes a new parallel clustering algorithm based on the k-means algorithm. It significantly reduces the exponential growth of computations. The proposed algorithm splits a dataset into batches while preserving the characteristics of the initial dataset and increasing the clustering speed. The idea is to define cluster centroids, which are also clustered, for each batch. According to the obtained centroids, the data points belong to the cluster with the nearest centroid. Real large datasets are used to conduct the experiments to evaluate the effectiveness of the proposed approach. The proposed approach is compared with k-means and its modification. The experiments show that the proposed algorithm is a promising tool for clustering large datasets in comparison with the k-means algorithm.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2468-2322
2468-6557
2468-2322
DOI:10.1049/trit.2019.0048