Abstract
The Harmony Search (HS) algorithm in recent years has been applied in many applications in computer science and engineering. This chapter is intended to review the application of the HS method in the area of web document clustering. Clustering is a problem of great practical importance that has been the focus of substantial research in several domains for decades. It is defined as the problem of partitioning data objects into groups, such that objects in the same group are similar, while objects in different groups are dissimilar. Due to the high-dimension and sparseness properties of documents the problem of clustering becomes more challenging when we apply it on web documents. Two algorithms in literature were proposed for clustering web documents with HS which will be reviewed in this chapter. Also three hybridization of HS based clustering with K-means algorithm will be reviewed. It will be shown that the HS method can outperform other methods in terms of solution quality and computational time.
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Forsati, R., Mahdavi, M. (2010). Web Text Mining Using Harmony Search. In: Geem, Z.W. (eds) Recent Advances In Harmony Search Algorithm. Studies in Computational Intelligence, vol 270. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04317-8_5
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DOI: https://doi.org/10.1007/978-3-642-04317-8_5
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