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Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc)

Ali Sabah1, Sabrina Tiun1, Nor Samsiah Sani1

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This study introduces a novel multiview multirepresentation consensus clustering ensemble (MMCC) method to enhance web search result clustering (WSRC). The MMCC method improves clustering quality by combining diverse data representations for better performance.

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Area of Science:

  • Information Retrieval
  • Data Mining
  • Machine Learning

Background:

  • Traditional text clustering methods often rely on single data representations (single-view), limiting their effectiveness.
  • Existing multiview approaches typically use representations of the same nature, failing to leverage diverse data characteristics.

Purpose of the Study:

  • To develop an enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method for improving web search result clustering (WSRC).
  • To create diverse candidate clustering solutions by integrating multiple data views (semantic, lexical, topic) and employing consensus clustering.

Main Methods:

  • Acquisition and preprocessing of standard datasets (MORESQUE, Open Directory Project-239).
  • Application of multiview multirepresentation clustering models.
  • Utilizing a radius-based cluster number estimation algorithm.
  • Employing a consensus clustering ensemble method to select high-quality overlapping clusters.

Main Results:

  • Multiview multirepresentation significantly improves clustering performance compared to single-view methods.
  • The proposed MMCC model demonstrates superior overall performance in WSRC compared to existing single-view clustering models.

Conclusions:

  • Integrating diverse data views (semantic, lexical, topic) through a multiview multirepresentation approach enhances clustering quality.
  • The MMCC method offers a robust framework for improving the accuracy and effectiveness of web search result clustering.