Add to ORKGThe aim of this work is to compare different strategies to cluster large data sets. In particular, the performance of the classical K-means algorithm and two strategies which combine different clustering procedures in a sequential way, are investigated through the analysis of a real-life data set consisting of approximately 1.5 million units
AbstractClustering plays a very vital role in exploring data, creating predictions and to overcome t...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
Abstract Cluster analysis has become one of the main tools used in extracting knowledge from data, w...
The exploratory nature of data analysis and data mining makes clustering one of the most usual tasks...
The exploratory nature of data analysis and data mining makes clustering one of the most usual tasks...
huge data is a big challenge. Clustering technique is able to find hidden patterns and to extract us...
The aim of this study is to analyze different strategies to cluster large data sets derived from soc...
Abstract- Clustering is the unsupervised classification of patterns (data items) into groups (cluste...
Advances in recent techniques for scientific data collection in the era of big data allow for the sy...
Due to current data collection technology, our ability to gather data has surpassed our ability to a...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
As we know that clustering is a process for discovering groups and identifying interesting patterns....
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
AbstractClustering plays a very vital role in exploring data, creating predictions and to overcome t...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
Abstract Cluster analysis has become one of the main tools used in extracting knowledge from data, w...
The exploratory nature of data analysis and data mining makes clustering one of the most usual tasks...
The exploratory nature of data analysis and data mining makes clustering one of the most usual tasks...
huge data is a big challenge. Clustering technique is able to find hidden patterns and to extract us...
The aim of this study is to analyze different strategies to cluster large data sets derived from soc...
Abstract- Clustering is the unsupervised classification of patterns (data items) into groups (cluste...
Advances in recent techniques for scientific data collection in the era of big data allow for the sy...
Due to current data collection technology, our ability to gather data has surpassed our ability to a...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
As we know that clustering is a process for discovering groups and identifying interesting patterns....
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
AbstractClustering plays a very vital role in exploring data, creating predictions and to overcome t...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...