The Web Mining Framework Combining Association Rules And Fuzzy Clusters
碩士 === 元智大學 === 資訊管理研究所 === 89 === Lately, most studies have relied on statistic clustering techniques to analyze web user profile data in web mining. However, this approach can only sort each user session into a single cluster. That is, it ignores a user session may contain several browsing prefers...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2001
|
Online Access: | http://ndltd.ncl.edu.tw/handle/65551909349006885595 |
id |
ndltd-TW-089YZU00396005 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-089YZU003960052015-10-13T12:14:43Z http://ndltd.ncl.edu.tw/handle/65551909349006885595 The Web Mining Framework Combining Association Rules And Fuzzy Clusters 結合關聯法則與模糊叢聚之網際探勘架構 Kung Wei Chang 張恭維 碩士 元智大學 資訊管理研究所 89 Lately, most studies have relied on statistic clustering techniques to analyze web user profile data in web mining. However, this approach can only sort each user session into a single cluster. That is, it ignores a user session may contain several browsing prefers. According to this insufficiency, fuzzy clustering techniques were proposed instead. But those methods only can use similarity score of session to calculate the similarity between pages. Therefore, if users browse the same web page by different paths, that causes wrong results. This research proposes a framework which combines the fuzzy clustering and association rules. This approach filters out the noisy data, and employs association rules to calculate the confidence of the rule as the association between different URL addresses. Finally, an improved fuzzy clustering is adopted, which replaces the similarity score of session with the confidence between pages, to found out the user prefers effectively. Yu-Chin Liu 劉俞志 2001 學位論文 ; thesis 44 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 元智大學 === 資訊管理研究所 === 89 === Lately, most studies have relied on statistic clustering techniques to analyze web user profile data in web mining. However, this approach can only sort each user session into a single cluster. That is, it ignores a user session may contain several browsing prefers. According to this insufficiency, fuzzy clustering techniques were proposed instead. But those methods only can use similarity score of session to calculate the similarity between pages. Therefore, if users browse the same web page by different paths, that causes wrong results.
This research proposes a framework which combines the fuzzy clustering and association rules. This approach filters out the noisy data, and employs association rules to calculate the confidence of the rule as the association between different URL addresses. Finally, an improved fuzzy clustering is adopted, which replaces the similarity score of session with the confidence between pages, to found out the user prefers effectively.
|
author2 |
Yu-Chin Liu |
author_facet |
Yu-Chin Liu Kung Wei Chang 張恭維 |
author |
Kung Wei Chang 張恭維 |
spellingShingle |
Kung Wei Chang 張恭維 The Web Mining Framework Combining Association Rules And Fuzzy Clusters |
author_sort |
Kung Wei Chang |
title |
The Web Mining Framework Combining Association Rules And Fuzzy Clusters |
title_short |
The Web Mining Framework Combining Association Rules And Fuzzy Clusters |
title_full |
The Web Mining Framework Combining Association Rules And Fuzzy Clusters |
title_fullStr |
The Web Mining Framework Combining Association Rules And Fuzzy Clusters |
title_full_unstemmed |
The Web Mining Framework Combining Association Rules And Fuzzy Clusters |
title_sort |
web mining framework combining association rules and fuzzy clusters |
publishDate |
2001 |
url |
http://ndltd.ncl.edu.tw/handle/65551909349006885595 |
work_keys_str_mv |
AT kungweichang thewebminingframeworkcombiningassociationrulesandfuzzyclusters AT zhānggōngwéi thewebminingframeworkcombiningassociationrulesandfuzzyclusters AT kungweichang jiéhéguānliánfǎzéyǔmóhúcóngjùzhīwǎngjìtànkānjiàgòu AT zhānggōngwéi jiéhéguānliánfǎzéyǔmóhúcóngjùzhīwǎngjìtànkānjiàgòu AT kungweichang webminingframeworkcombiningassociationrulesandfuzzyclusters AT zhānggōngwéi webminingframeworkcombiningassociationrulesandfuzzyclusters |
_version_ |
1716855660991217664 |