A Document Clustering Method Based on Hierarchical Algorithm with Model Clustering

Liu, Zhihui; Kong, Lingjun

doi:10.1109/waina.2008.45

Cited by 10 publications

(6 citation statements)

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“…We showed that the chord gap divergence centroid can be obtained using a convex-concave iterative procedure [7], and analyzed the k-means++ [31] performance by giving the Taylor-Lagrange forms of the skew Jensen and chord gap divergences. We expect our contributions to be useful for the signal processing, information fusion and machine learning communities where the Bhattacharrya [34,35] or Chernoff information [2,16] is often used. In practice, the triparametric chord gap divergence shall be tuned according to the application at hand (and the dataset for supervised tasks using cross-validation for example).…”

Section: Discussionmentioning

confidence: 99%

A generalization of the Jensen divergence: The chord gap divergence

Nielsen

2017

Preprint

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We introduce a novel family of distances, called the chord gap divergences, that generalizes the Jensen divergences (also called the Burbea-Rao distances), and study its properties. It follows a generalization of the celebrated statistical Bhattacharyya distance that is frequently met in applications. We report an iterative concave-convex procedure for computing centroids, and analyze the performance of the k-means++ clustering with respect to that new dissimilarity measure by introducing the Taylor-Lagrange remainder form of the skew Jensen divergences.

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Section: Discussionmentioning

confidence: 99%

A generalization of the Jensen divergence: The chord gap divergence

Nielsen

2017

Preprint

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“…A document clustering method based on hierarchical algorithm with model clustering is presented by Haojun et al, [12]. This paper involved in analyzing and making use of cluster overlapping technique to design cluster merging criterion.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Ontology for Distributed Document Clustering based on Genetic Algorithm

Thangamani¹,

Thangaraj²

2013

Appl. Math. Inf. Sci.

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The availability of large quantity of text documents from the World Wide Web and business document management systems has made the dynamic separation of texts into new categories as a very important task for every business intelligence systems. But, present text clustering algorithms still suffer from problems of practical applicability. Recent studies have shown that, in order to improve the performance of document clustering, ontologies are useful. Ontology is nothing but the conceptualization of a domain into an individual identifiable format, but machine-readable format containing entities, attributes, relationships and axioms. By analyzing all types of techniques for document clustering, a clustering technique depending on Genetic Algorithm (GA) is determined to be better as GA is a global convergence technique and has the ability of determining the most suitable cluster centers without difficulties. In this paper, a new document clustering scheme with fuzzy ontology based genetic clustering is proposed. The experimental results reveal that the proposed approach increases the accuracy to a large extent and the clustering time is also highly reduced.

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“…There are two main categories of the document clustering algorithm that is Partitioned Clustering [6,7,8] and Hierarchical Clustering. Hierarchical clustering [10,11] Another clustering approach is the partitioned clustering algorithm, which creates one level partitioning of the document collection. K-means is an example of partitioned clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Mostly, the Agglomerative Hierarchical Clustering [10,11] and K-means clustering [6,7] cannot produce the effective results for clustering the documents. Some of the clustering algorithm produces the descriptive summaries that are not meaningful and readable which is difficult and reduces the search range.…”

Section: Related Workmentioning

confidence: 99%

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An Analytical Assessment on Document Clustering

Pushplata¹,

Chatterjee²

2012

IJCNIS

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Clustering is related to data mining for information retrieval. Relevant information is retrieved quickly while doing the clustering of documents. It organizes the documents into groups; each group contains the documents of similar type content. Document clustering is an unsupervised approach of data mining. Different clustering algorithms are used for clustering the documents such as partitioned clustering (K-means Clustering) and Hierarchical Clustering (Agglomerative Hierarchical Clustering (AHC)). This paper presents analysis of Suffix Tree Clustering (STC) Algorithm and other clustering techniques (K-means, AHC) that are being done in literature survey. The paper also focuses on traditional Vector Space Model (VSM) for similarity measures, which is used for clustering the documents. This paper also focuses on the comparison of different clustering algorithms. STC algorithm improves the searching performance as compare to other clustering algorithms as the papers studied in literature survey. The paper presents STC algorithm applied on the search result documents, which is stored in the dataset. This paper articulates the key requirements for web document clustering and clusters would be created on the full text of the web documents. STC perform the clustering and make the clusters based on phrases shared between the documents. STC is faster clustering algorithm for document clustering.

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A Document Clustering Method Based on Hierarchical Algorithm with Model Clustering

Cited by 10 publications

References 6 publications

A generalization of the Jensen divergence: The chord gap divergence

A generalization of the Jensen divergence: The chord gap divergence

Fuzzy Ontology for Distributed Document Clustering based on Genetic Algorithm

An Analytical Assessment on Document Clustering

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