Bidirectional hierarchical clustering for Web mining

Yao, Zhen; Choi, Ben

doi:10.1109/wi.2003.1241281

Cited by 12 publications

(8 citation statements)

References 18 publications

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“…As similar terms will be clustered into the same node, outliers will eventually be singled out as individual terms in leaf nodes. Consequently, unlike some conventional methods such as K-means (Yao and Choi 2003), clustering using TTA is not susceptible to poor results due to outliers. In fact, there are two ways of looking at the term "Google", one as result in two clusters, three clusters, and five clusters, respectively.…”

Section: Evaluations and Discussionmentioning

confidence: 99%

Tree-Traversing Ant Algorithm for term clustering based on featureless similarities

Wong

Liu

Bennamoun

2007

Data Min Knowl Disc

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Many conventional methods for concepts formation in ontology learning have relied on the use of predefined templates and rules, and static resources such as WordNet. Such approaches are not scalable, difficult to port between different domains and incapable of handling knowledge fluctuations. Their results are far from desirable, either. In this paper, we propose a new antbased clustering algorithm, Tree-Traversing Ant (TTA), for concepts formation as part of an ontology learning system. With the help of Normalized Google Distance (NGD) and n • of Wikipedia (n • W) as measures for similarity and distance between terms, we attempt to achieve an adaptable clustering method that is highly scalable and portable across domains. Evaluations with an seven datasets show promising results with an average lexical overlap of 97% and ontological improvement of 48%. At the same time, the evaluations demonstrated several advantages that are not simultaneously present in standard ant-based and other conventional clustering methods.

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Section: Evaluations and Discussionmentioning

confidence: 99%

Tree-Traversing Ant Algorithm for term clustering based on featureless similarities

Wong

Liu

Bennamoun

2007

Data Min Knowl Disc

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“…Some of the well‐known clustering methods include partitioning algorithms based on dividing entire data into dissimilar groups, hierarchical methods, density‐ and grid‐based clustering approaches, and graph‐based methods (Barthelemy and Leclerc 1995; Jain et al 1999; Grira, Crucianu, and Boujemaa 2005; Xu and Wunsch 2005). Due to the high dimensionality and lack of orthogonality of Web document vectors, algorithms such as K‐Means (McQueen 1967), hierarchical agglomerative clustering (HAC) (Day and Edelsbrunner 1985), and graph partitioning methods have gained more popularity and applicability in the Web environment (Yao and Choi 2003).…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Heuristics for Hierarchical Web Data Clustering

Chehreghani

Abolhassani

2012

Computational Intelligence

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Clustering Web data is one important technique for extracting knowledge from the Web. In this paper, a novel method is presented to facilitate the clustering. The method determines the appropriate number of clusters and provides suitable representatives for each cluster by inference from a Bayesian network. Furthermore, by means of the Bayesian network, the contents of the Web pages are converted into vectors of lower dimensions. The method is also extended for hierarchical clustering, and a useful heuristic is developed to select a good hierarchy. The experimental results show that the clusters produced benefit from high quality.

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“…Oftentimes, some sort of hybrid algorithm is developed for a particular application. Real-world applications of hierarchical clustering can be found in the following references: speech recognition [14], web mining [15], lung cancer research [16], and document mining [17]. A wide variety of techniques are presented in these papers.…”

Section: Figurementioning

confidence: 99%