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https://ah.lib.nccu.edu.tw/handle/140.119/75893
題名: | DSM-PLW: Single-pass mining of path traversal patterns over streaming Web click-sequences | 作者: | Shan, Man-kwan;Li, Hua-fu;Lee, Suh-yin 沈錳坤 |
貢獻者: | 資科系 | 關鍵詞: | Web click-sequence streams; Path traversal patterns; Single-pass algorithm | 日期: | 2006 | 上傳時間: | 17-Jun-2015 | 摘要: | Mining Web click streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some interesting characteristics, such as unknown or unbounded length, possibly a very fast arrival rate, inability to backtrack over previously arrived click-sequences, and a lack of system control over the order in which the data arrive. In this paper, we propose a projection-based, single-pass algorithm, called DSM-PLW (Data Stream Mining for Path traversal patterns in a Landmark Window), for online incremental mining of path traversal patterns over a continuous stream of maximal forward references generated at a rapid rate. According to the algorithm, each maximal forward reference of the stream is projected into a set of reference-suffix maximal forward references, and these reference-suffix maximal forward references are inserted into a new in-memory summary data structure, called SP- forest (Summary Path traversal pattern forest), which is an extended prefix tree-based data structure for storing essential information about frequent reference sequences of the stream so far. The set of all maximal reference sequences is deter- mined from the SP-forest by a depth-first-search mechanism, called MRS-mining (Maximal Reference Sequence mining). Theoretical analysis and experimental studies show that the proposed algorithm has gently growing memory requirements and makes only one pass over the streaming data. � 2005 Elsevier B.V. All rights reserved. | 關聯: | Computer Networks - COMPUT NETW , vol. 50, no. 10, pp. 1474-1487 | 資料類型: | article | DOI: | http://dx.doi.org/10.1016/j.comnet.2005.10.018 |
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