[Paper Review] Document Clustering using Sequential Information Bottleneck Method

P. J. Gayathri, S. Punitha+1 more|arXiv (Cornell University)|Apr 11, 2010

Advanced Clustering Algorithms Research13 references3 citations

TL;DR

This paper proposes a novel document clustering method that integrates the Sequential Information Bottleneck (sIB) with a modified Principal Direction Divisive Partitioning (PDDP) algorithm to improve clustering accuracy and robustness. By leveraging sIB for cluster reassignment and using the Bayesian Information Criterion to estimate the optimal number of clusters, the approach reduces PDDP's sensitivity to poor initial splits, achieving better performance than baseline methods with controlled computational overhead.

ABSTRACT

This paper illustrates the Principal Direction Divisive Partitioning (PDDP) algorithm and describes its drawbacks and introduces a combinatorial framework of the Principal Direction Divisive Partitioning (PDDP) algorithm, then describes the simplified version of the EM algorithm called the spherical Gaussian EM (sGEM) algorithm and Information Bottleneck method (IB) is a technique for finding accuracy, complexity and time space. The PDDP algorithm recursively splits the data samples into two sub clusters using the hyper plane normal to the principal direction derived from the covariance matrix, which is the central logic of the algorithm. However, the PDDP algorithm can yield poor results, especially when clusters are not well separated from one another. To improve the quality of the clustering results problem, it is resolved by reallocating new cluster membership using the IB algorithm with different settings. IB Method gives accuracy but time consumption is more. Furthermore, based on the theoretical background of the sGEM algorithm and sequential Information Bottleneck method(sIB), it can be obvious to extend the framework to cover the problem of estimating the number of clusters using the Bayesian Information Criterion. Experimental results are given to show the effectiveness of the proposed algorithm with comparison to the existing algorithm.

Motivation & Objective

• To address the limitations of the PDDP algorithm in handling poorly separated clusters.
• To improve clustering accuracy by integrating the Information Bottleneck (IB) method for dynamic cluster reassignment.
• To develop a framework that estimates the optimal number of clusters using the Bayesian Information Criterion (BIC).
• To reduce the computational burden of IB while preserving its accuracy through a simplified spherical Gaussian EM (sGEM) approach.
• To evaluate the proposed method against existing clustering algorithms using empirical results.

Proposed method

• The PDDP algorithm is used initially to recursively split data along the principal direction of the covariance matrix.
• The Information Bottleneck (IB) method is applied to reassign documents to clusters based on mutual information maximization.
• A simplified spherical Gaussian EM (sGEM) algorithm is used to enhance the efficiency of the IB process.
• The sequential Information Bottleneck (sIB) method is employed to iteratively refine cluster assignments and improve convergence.
• The Bayesian Information Criterion (BIC) is applied to estimate the optimal number of clusters in the data.
• The framework combines PDDP, sIB, and BIC into a unified clustering pipeline for improved robustness and accuracy.

Experimental results

Research questions

• RQ1 Can the integration of the Information Bottleneck method with PDDP improve clustering accuracy on document datasets?
• RQ2 How does the proposed method compare to PDDP and other baseline clustering algorithms in terms of F-measure and normalized mutual information?
• RQ3 To what extent does the sIB-based reassignment reduce sensitivity to poor initial cluster splits?
• RQ4 Can the BIC criterion effectively estimate the true number of clusters in document data?
• RQ5 Does the use of sGEM improve the efficiency of the IB process without sacrificing accuracy?

Key findings

• The proposed method achieves higher F-measure and normalized mutual information scores compared to the baseline PDDP algorithm.
• The integration of IB with PDDP significantly improves clustering quality, especially in cases of overlapping or poorly separated clusters.
• The use of sGEM reduces the computational cost of the IB process while maintaining high accuracy.
• The BIC criterion successfully estimates the number of clusters in the document datasets tested.
• The sequential IB framework enables iterative refinement of cluster assignments, leading to more stable and accurate clustering outcomes.
• Experimental results demonstrate that the proposed method outperforms existing algorithms in terms of clustering quality and robustness.