Subjective Association Rule Mining
| Autor: | Pu-Tai Yang, Ching-Chi Chen, Kai-Hao Yang, Shwu-Min Horng |
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| Rok vydání: | 2018 |
| Předmět: |
Sequence
Information retrieval Point (typography) Association rule learning Computer science business.industry Big data 02 engineering and technology Interval (mathematics) Preference Core (game theory) Ranking 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business |
| Zdroj: | ICMLC |
| DOI: | 10.1145/3195106.3195174 |
| Popis: | Human behavior is complex and ambiguous. This study investigates how human beings consider two significant elements of discrete items: one side is their preference ranking relationships, while the other is their preferred temporal relationships. The development of a means of deriving valuable information from collected user opinions of these two relationships is therefore a core problem in data mining. This study proposes a novel model which simultaneously considers a heterogeneous database of point-based preference ranking relationships and interval-based temporal relationships to discover their association rules, while the interval-based relationships refer to occurrences with beginning and end points. An early-stage survey experiment, conducted to collect authentic users' subjective opinions for discovering the management implications, is also presented in this study. |
| Databáze: | OpenAIRE |
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