Technical Reports - Query Results
Your query term was 'number = 2001-09'1 report found
- OFAI-TR-2001-09 (
88kB g-zipped PostScript file, 
238kB PDF file)Detecting Temporal Change in Event Sequences: An Application to Demographic Data
- Hendrik Blockeel, Johannes Fürnkranz, Alexia Prskawetz, Francesco C. Billari
- In this paper, we discuss an approach for discovering temporal
changes in event sequences, and present first results from a study
on demographic data. The data encode characteristic events in a
person's life course, such as their birth date, the begin and end
dates of their partnerships and marriages, and the birth dates of
their children. The goal is to detect significant changes in the
chronology of these events over people from different birth cohorts.
To solve this problem, we encoded the temporal information in a
first-order logic representation, and employed Warmr, an ILP system
that discovers association rules in a multi-relational data set, to
detect frequent patterns that show significant variance over
different birth cohorts. As a case study in multi-relational
association rule mining, this work illustrates the flexibility
resulting from the use of first-order background knowledge, but also
uncovers a number of important issues that hitherto received little
attention.
Keywords: Data Mining, Association Rules, Temporal Patterns, Inductive Logic Programming, Demography, Life Course Analysis
- In this paper, we discuss an approach for discovering temporal
changes in event sequences, and present first results from a study
on demographic data. The data encode characteristic events in a
person's life course, such as their birth date, the begin and end
dates of their partnerships and marriages, and the birth dates of
their children. The goal is to detect significant changes in the
chronology of these events over people from different birth cohorts.
To solve this problem, we encoded the temporal information in a
first-order logic representation, and employed Warmr, an ILP system
that discovers association rules in a multi-relational data set, to
detect frequent patterns that show significant variance over
different birth cohorts. As a case study in multi-relational
association rule mining, this work illustrates the flexibility
resulting from the use of first-order background knowledge, but also
uncovers a number of important issues that hitherto received little
attention.