Document Clustering Using Semantic Kernels Based on Term-Term Correlations
Published 1 December 2009
Ahmed Farahat, Mohamed S. Kamel
Citations9
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This paper analyzes semantic kernels that are based on term-term correlations that capture different aspects of correlations between terms, and shows that the proposed method achieves significant improvement in document clustering compared to VSM.
Abstract
Document clustering algorithms usually use vector space model (VSM) as their underlying model for document representation. VSM assumes that terms are independent and accordingly ignores any semantic relations between them. This results in mapping documents to a space where the proximity between document vectors does not reflect their true semantic similarity. In this paper, we propose the use of semantic kernels that are based on term-term correlations for improving the effectiveness of document clustering algorithms. The used kernels measure proximity between documents based on how their terms are statistically correlated. We analyze semantic kernels that capture different aspects of correlations between terms, and evaluate them by conducting experiments on different benchmark data sets. Results show that the proposed method achieves significant improvement in document clustering compared to VSM.
Keywords
Computer Science
ACM Computing SurveysData clustering
13,184 Citations1999Anil K. Jain, M. Narasimha Murty +1 more
An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Journal of the American Society for Information ScienceIndexing by latent semantic analysis
12,677 Citations1990Scott Deerwester, Susan Dumais +3 more
Statistics and ComputingA tutorial on spectral clustering
10,269 Citations2007Ulrike von Luxburg
This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches.
Communications of the ACMA vector space model for automatic indexing
7,434 Citations1975Gerard Salton, Anita M.-Y. Wong +1 more
An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents, demonstating the usefulness of the model.
Cambridge University Press eBooksKernel Methods for Pattern Analysis
6,598 Citations2004John Shawe‐Taylor, Nello Cristianini
This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Document clustering based on non-negative matrix factorization
1,847 Citations2003Wei Xu, Xin Liu +1 more
This paper proposes a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus that surpasses the latent semantic indexing and the spectral clustering methods not only in the easy and reliable derivation of document clustered results, but also in document clusters accuracies.
Co-clustering documents and words using bipartite spectral graph partitioning
1,696 Citations2001Inderjit S. Dhillon
A new spectral co-clustering algorithm is used that uses the second left and right singular vectors of an appropriately scaled word-document matrix to yield good bipartitionings and it can be shown that the singular vectors solve a real relaxation to the NP-complete graph bipartitionsing problem.
Machine LearningConcept Decompositions for Large Sparse Text Data Using Clustering
1,297 Citations2001Inderjit S. Dhillon, Dharmendra S. Modha
The concept vectors produced by the spherical k-means algorithm constitute a powerful sparse and localized “basis” for text data sets and are localized in the word space, are sparse, and tend towards orthonormality.
Machine LearningEmpirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering
613 Citations2004Ying Zhao, George Karypis
This paper evaluates the performance of different criterion functions in the context of partitional clustering algorithms for document datasets, and shows that there are a set of criterion functions that consistently outperform the rest.
CLUTO - A Clustering Toolkit
551 Citations2002George Karypis
Clustering algorithms divide data into meaningful or useful groups, called clusters, such that the intra-cluster similarity is maximized and the inter-clusters similarity is minimized.
Document clustering using word clusters via the information bottleneck method
476 Citations2000Noam Slonim, Naftali Tishby
A novel implementation of the recently introduced information bottleneck method for unsupervised document clustering that first finds word-clusters that capture most of the mutual information about to set of documents, and then finds document clusters that preserve the information about the word clusters.
WordNet improves text document clustering
272 Citations2003Andreas Hotho, Steffen Staab +1 more
This work integrates background knowledge — in the authors' application Wordnet — into the process of clustering text documents, and clusters the documents by a standard partitional algorithm.
Generalized vector spaces model in information retrieval
267 Citations1985S. K. M. Wong, Wojciech Ziarko +1 more
This paper proposes a systematic method (the generalized vector space model) to compute term correlations directly from automatic indexing scheme and demonstrates how such correlations can be included with minimal modification in the existing vector based information retrieval systems.
Projections for efficient document clustering
214 Citations1997Hinrich Schütze, Craig Silverstein
It is concluded that truncation yields clusters as good as those produced by full-profile clustering while offering a significant speed advantage.
Feature Selection and Document Clustering
94 Citations2004Inderjit S. Dhillon, Jacob Kogan +1 more
This chapter suggests two techniques for feature or term selection along with a number of clustering strategies that significantly reduce the dimension of the vector space model.
Lecture notes in computer scienceClustering Documents Using a Wikipedia-Based Concept Representation
81 Citations2009Anna Y.Q. Huang, David Milne +2 more
This paper shows how Wikipedia and the semantic knowledge it contains can be exploited for document clustering by creating a concept-based document representation and developing a similarity measure that evaluates the semantic relatedness between concept sets for two documents.
ACM SIGIR ForumProjections for efficient document clustering
53 Citations1997Hinrich Schütze, Craig Silverstein
UWSpace (University of Waterloo)Cooperative clustering model and its applications
14 Citations2008Rasha Kashef
A novel Cooperative Clustering Outliers Detection (CCOD) algorithm is implemented through the implication of the cooperation methodology for better detection of outliers in data and achieves decent speedup by increasing number of nodes.