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WeDoCWT: A New Method for Web Document Clustering Using Discrete Wavelet Transforms

Author

Listed:
  • Hanan Al-Mofareji

    (Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 23218, Saudi Arabia)

  • Mahmoud Kamel

    (Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 23218, Saudi Arabia)

  • Mohamed Y. Dahab

    (Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 23218, Saudi Arabia)

Abstract

Organizing web information is an important aspect of finding information in the easiest and most efficient way. We present a new method for web document clustering called WeDoCWT, which exploits the discrete wavelet transform and term signal, to improve the document representation. We studied different methods for document segmentation to construct the term signals. We used two datasets, UW-CAN and WebKB, to evaluate the proposed method. The experimental results indicated that dividing the documents into fixed segments is preferable to dividing them into logical segments based on HTML features because the web pages do not have the same structure. Mean TF–IDF reduction technique gives the best results in most cases. WeDoCWT gives F-measure better than most of the previous approaches described in the literature. We used Munkres assignment algorithm to assign each produced cluster to the original class in order to evaluate the clustering results.

Suggested Citation

  • Hanan Al-Mofareji & Mahmoud Kamel & Mohamed Y. Dahab, 2017. "WeDoCWT: A New Method for Web Document Clustering Using Discrete Wavelet Transforms," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-19, March.
    • Handle: RePEc:wsi:jikmxx:v:16:y:2017:i:01:n:s0219649217500046
    DOI: 10.1142/S0219649217500046
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    References listed on IDEAS

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    1. Chaker Jebari, 2016. "Multi-Label Genre Classification of Web Pages Using an Adaptive Centroid-Based Classifier," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 1-21, March.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
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    Cited by:

    1. Yasmine Lamari & Said Chah Slaoui, 2018. "PDC-Transitive: An Enhanced Heuristic for Document Clustering Based on Relational Analysis Approach and Iterative MapReduce," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-18, June.
    2. Saida Ishak Boushaki & Nadjet Kamel & Omar Bendjeghaba, 2018. "High-Dimensional Text Datasets Clustering Algorithm Based on Cuckoo Search and Latent Semantic Indexing," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 1-24, September.

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