Paper Info
Title
Mining Association Rules from Semi-Structured Data
Abstract
Despite the growing popularity of semi-structured data such as Web documents,most knowledge discovery research has focused on databases containing well structured data. In this paper, we try to find useful information from semi- structured data. In our approach, we begin by represent- ing semi-structured data in a prototype-based approach. We then detect the most typical common structure of semi- structured data and re-store the data into this structure. We can consider this common structure as a structured layer. The structured layer filter s out useless properties of semi- structured data. Next, we apply the algorithm of mining association rules to the structured layer by using the idea of concept hierarchy. Concept hierarchy maintains rela- tionships between concepts. The use of concept hierarchy allows us to generate extra rules in addition to originally generated rules. The extra rules contain related concepts with the concepts in the original rules. These extra rules are often more informative and useful for finding patterns. In this way, some kind of knowledge can be extracted from semi-structured data.
Year
Venue
Keywords
2000
ICDCS Workshop of Knowledge Discovery and Data Mining in the World-Wide Web
structured data,semi structured data,knowledge discovery,association rule
Field
DocType
Citations 
Semi-structured data,Data mining,Information retrieval,Computer science,Association rule learning,Knowledge extraction
Conference
4
PageRank 
References 
Authors
0.50
9
2
Name
Order
Citations
PageRank
Kohei Maruyama151.24
Kuniaki Uehara249184.76