Discovering Structural Association of Semistructured Data

  • Authors:
  • Ke Wang;Huiqing Liu

  • Affiliations:
  • -;-

  • Venue:
  • IEEE Transactions on Knowledge and Data Engineering
  • Year:
  • 2000

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Abstract

Many semistructured objects are similarly, though not identically, structured. We study the problem of discovering 驴typical驴 substructures of a collection of semistructured objects. The discovered structures can serve the following purposes: 1) the 驴table-of-contents驴 for gaining general information of a source, 2) a road map for browsing and querying information sources, 3) a basis for clustering documents, 4) partial schemas for providing standard database access methods, and 5) user/customer's interests and browsing patterns. The discovery task is impacted by structural features of semistructured data in a nontrivial way and traditional data mining frameworks are inapplicable. We define this discovery problem and propose a solution.