The aim of this study is to analyze different strategies to cluster large data sets derived from social context. For the purpose of clustering, trials on effective and efficient methods for large databases have only been carried out in recent years due to the emergence of the field of data mining. In this paper a sequential approach based on multiobjective genetic algorithm as clustering technique is proposed. The proposed strategy is applied to a real-life data set consisting of approximately 1.5 million workers and the results are compared with those obtained by other methods to find out an unambiguous partitioning of data.
The aim of this study is to analyze different strategies to cluster large data sets derived from social context. For the purpose of clustering, trials on effective and efficient methods for large databases have only been carried out in recent years due to the emergence of the field of data mining. In this paper a sequential approach based on multiobjective genetic algorithm as clustering technique is proposed. The proposed strategy is applied to a real-life data set consisting of approximately 1.5 million workers and the results are compared with those obtained by other methods to find out an unambiguous partitioning of data.
Clustering Large Data Set: An Applied Comparative Study / Bocci, Laura; Mingo, Isabella. - STAMPA. - 1(2012), pp. 3-12. [10.1007/978-3-642-21037-2_1].
Clustering Large Data Set: An Applied Comparative Study
BOCCI, Laura;MINGO, Isabella
2012
Abstract
The aim of this study is to analyze different strategies to cluster large data sets derived from social context. For the purpose of clustering, trials on effective and efficient methods for large databases have only been carried out in recent years due to the emergence of the field of data mining. In this paper a sequential approach based on multiobjective genetic algorithm as clustering technique is proposed. The proposed strategy is applied to a real-life data set consisting of approximately 1.5 million workers and the results are compared with those obtained by other methods to find out an unambiguous partitioning of data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
