TY - JOUR
T1 - Novel meta-heuristic algorithms for clustering web documents
AU - Mahdavi, M.
AU - Chehreghani, M. Haghir
AU - Abolhassani, H.
AU - Forsati, R.
N1 - Funding Information:
This research was in part supported by a grant from IPM. (No. CS1386-4-06).
PY - 2008/7/15
Y1 - 2008/7/15
N2 - Clustering the web documents is one of the most important approaches for mining and extracting knowledge from the web. Recently, one of the most attractive trends in clustering the high dimensional web pages has been tilt toward the learning and optimization approaches. In this paper, we propose novel hybrid harmony search (HS) based algorithms for clustering the web documents that finds a globally optimal partition of them into a specified number of clusters. By modeling clustering as an optimization problem, first, we propose a pure harmony search-based clustering algorithm that finds near global optimal clusters within a reasonable time. Then, we hybridize K-means and harmony clustering in two ways to achieve better clustering. Experimental results reveal that the proposed algorithms can find better clusters when compared to similar methods and also illustrate the robustness of the hybrid clustering algorithms.
AB - Clustering the web documents is one of the most important approaches for mining and extracting knowledge from the web. Recently, one of the most attractive trends in clustering the high dimensional web pages has been tilt toward the learning and optimization approaches. In this paper, we propose novel hybrid harmony search (HS) based algorithms for clustering the web documents that finds a globally optimal partition of them into a specified number of clusters. By modeling clustering as an optimization problem, first, we propose a pure harmony search-based clustering algorithm that finds near global optimal clusters within a reasonable time. Then, we hybridize K-means and harmony clustering in two ways to achieve better clustering. Experimental results reveal that the proposed algorithms can find better clusters when compared to similar methods and also illustrate the robustness of the hybrid clustering algorithms.
UR - http://www.scopus.com/inward/record.url?scp=44649098316&partnerID=8YFLogxK
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U2 - 10.1016/j.amc.2007.12.058
DO - 10.1016/j.amc.2007.12.058
M3 - Article
AN - SCOPUS:44649098316
VL - 201
SP - 441
EP - 451
JO - Applied Mathematics and Computation
JF - Applied Mathematics and Computation
SN - 0096-3003
IS - 1-2
ER -