PDF (1MB) - QUT ePrints

22 · Alsayed Algergawy et al.
Experimental evaluation reported in [Tran et al. 2008; Yang et al. 2009] shows that combining
the structure and content features to compute the similarity between XML documents
outperforms the other two methods, especially when documents belong to different structural
definitions.
5. CLUSTERING/GROUPING
XML data that are similar in structures and semantics are grouped together to form a cluster
using a suitable clustering algorithm [Jain et al. 1999; Berkhin 2002; Xu and Wunsch
2005]. Clustering methods are generally divided into two broad categories: hierarchical
and non-hierarchical (partitional) methods. The non-hierarchical methods group a data set
into a number of clusters using a pairwise distance matrix that records the similarity between
each pair of documents in the data set, while the hierarchical methods produce nested
sets of data (hierarchies), in which pairs of elements or clusters are successively linked until
every element in the data set becomes connected. Many other clustering techniques
have been developed considering the more frequent requirement of tackling large-scale
and high-dimensional data sets in many recent applications.
5.1 Hierarchical Methods
Hierarchical clustering builds a hierarchy of clusters, known as a dendrogram. The root
node of the dendrogram represents the whole data set and each leaf node represents a
data item. This representation gives a well informative description and visualization of
the data clustering structure. Strategies for hierarchical clustering generally fall into two
types: agglomerative and division. The divisive clustering algorithm builds the hierarchical
solution from top toward the bottom by using a repeated cluster bisectioning approach.
Given a set of N XML data to be clustered, and an N × N similarity matrix, the basic
process of the agglomerative hierarchical clustering algorithm is:
(1) treat each XML data of the data set to be clustered as a cluster, so that if you have N
items, you now have N clusters, each containing just one XML data.
(2) find the most similar pair of clusters and merge them into a single cluster.
(3) compute similarities between the new cluster and each of the old clusters.
(4) repeat steps 2 and 3 until all items are clustered into a single cluster of size N.
Based on different methods used to compute the similarity between a pair of clusters (Step
3), there are many agglomerative clustering algorithms. Among them are the single-link
and complete-link algorithms [Jain et al. 1999; Manning et al. 2008]. In the single-link
method, the similarity between one cluster and another cluster is equal to the maximum
similarity from any member of one cluster to any member of the other cluster. In the
complete-link method, the similarity between one cluster and another cluster has to be
equal to the minimum similarity from any member of one cluster to any member of the
other cluster. Even if the single-link algorithm has a better time complexity (O(N 2 )) than
the time complexity (O(N 2 log N)) of the complete-link algorithm, it has been observed
that the complete-link algorithm produces more useful hierarchies in many applications
than the single-link algorithm. Further, the high cost for most of the hierarchical algorithms
limit their application in large-scale data sets. With the requirement for handling largescale
data sets, several new hierarchical algorithms have been proposed to improve the
ACM Computing Surveys, Vol. , No. , 2009.