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Mining Frequent Itemsets from Uncertain Data: Extensions to Constrained Mining and Stream Mining.

機(jī)譯：從不確定的數(shù)據(jù)中挖掘頻繁項(xiàng)集：約束挖掘和流挖掘的擴(kuò)展。

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摘要

Most studies on frequent itemset mining focus on mining precise data. However, there are situations in which the data are uncertain. This leads to the mining of uncertain data. There are also situations in which users are only interested in frequent itemsets that satisfy user-specified aggregate constraints. This leads to constrained mining of uncertain data. Moreover, floods of uncertain data can be produced in many other situations. This leads to stream mining of uncertain data. In this M.Sc. thesis, we propose algorithms to deal with all these situations. We first design a tree-based mining algorithm to find all frequent itemsets from databases of uncertain data. We then extend it to mine databases of uncertain data for only those frequent itemsets that satisfy user-specified aggregate constraints and to mine streams of uncertain data for all frequent itemsets. Experimental results show the effectiveness of all these algorithms.
機(jī)譯：大多數(shù)關(guān)于頻繁項(xiàng)集挖掘的研究都集中在精確數(shù)據(jù)的挖掘上。但是，在某些情況下數(shù)據(jù)不確定。這導(dǎo)致了不確定數(shù)據(jù)的挖掘。在某些情況下，用戶僅對(duì)滿足用戶指定的聚合約束的頻繁項(xiàng)目集感興趣。這導(dǎo)致對(duì)不確定數(shù)據(jù)的約束挖掘。此外，在許多其他情況下也會(huì)產(chǎn)生大量不確定的數(shù)據(jù)。這導(dǎo)致不確定數(shù)據(jù)的流挖掘。在這個(gè)碩士論文中，我們提出了應(yīng)對(duì)所有這些情況的算法。我們首先設(shè)計(jì)一種基于樹的挖掘算法，以從不確定數(shù)據(jù)的數(shù)據(jù)庫中查找所有頻繁項(xiàng)集。然后，我們將其擴(kuò)展到僅針對(duì)那些滿足用戶指定的聚合約束的頻繁項(xiàng)集挖掘不確定數(shù)據(jù)的數(shù)據(jù)庫，并針對(duì)所有頻繁項(xiàng)集挖掘不確定數(shù)據(jù)的流。實(shí)驗(yàn)結(jié)果證明了所有這些算法的有效性。

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  • 作者

    Hao, Boyu.;

  • 作者單位

    University of Manitoba (Canada).;

  • 授予單位 University of Manitoba (Canada).;
  • 學(xué)科 Computer Science.
  • 學(xué)位 M.Sc.
  • 年度 2010
  • 頁碼 141 p.
  • 總頁數(shù) 141
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關(guān)鍵詞

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