TELKOMNIKA Telecommunication, Computing, Electronics and Control
Cluster-based information retrieval by using (K-means)- hierarchical parallel genetic algorithms approach

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Cluster-based information retrieval by using (K-means)- hierarchical parallel genetic algorithms approach

Cluster-based IR
HPGA
Information retrieval
K-means
Web document clustering

Cluster-based information retrieval is one of the information retrieval (IR)
tools that organize, extract features and categorize the web documents
according to their similarity. Unlike traditional approaches, cluster-based IR is
fast in processing large datasets of document. To improve the quality of
retrieved documents, increase the efficiency of IR and reduce irrelevant
documents from user search. In this paper, we proposed a (K-means)-
hierarchical parallel genetic algorithms approach (HPGA) that combines the
K-means clustering algorithm with hybrid PG of multi-deme and master/slave
PG algorithms. K-means uses to cluster the population to k subpopulations
then take most clusters relevant to the query to manipulate in a parallel way by
the two levels of genetic parallelism, thus, irrelevant documents will not be
included in subpopulations, as a way to improve the quality of results. Three
common datasets (NLP, CISI, and CACM) are used to compute the recall,
precision, and F-measure averages. Finally, we compared the precision values
of three datasets with Genetic-IR and classic-IR. The proposed approach
precision improvements with IR-GA were 45% in the CACM, 27% in the
CISI, and 25% in the NLP. While, by comparing with Classic-IR, (K-means)-
HPGA got 47% in CACM, 28% in CISI, and 34% in NLP.

Sarah Hussein Toman, Mohammed Hamzah Abed, Zinah Hussein Toman

http://journal.uad.ac.id/index.php/TELKOMNIKA

Aug 29, 2020

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