Discovering Periodic-Frequent Patterns in Transactional Databases
作者： Syed Khairuzzaman Tanbeer , Chowdhury Farhan Ahmed , Byeong-Soo Jeong , Young-Koo Lee , None
DOI: 10.1007/978-3-642-01307-2_24
关键词: Data structure 、 Data mining 、 Database 、 Computer science 、 K-optimal pattern discovery 、 Knowledge extraction 、 Interval (mathematics) 、 Tree (data structure) 、 Set (abstract data type) 、 Scalability 、 Space (commercial competition)
摘要: Since mining frequent patterns from transactional databases involves an exponential mining space and generates a huge number of patterns, efficient discovery of user-interest-based frequent pattern set becomes the first priority for a mining algorithm. In many real-world scenarios it is often sufficient to mine a small interesting representative subset of frequent patterns. Temporal periodicity of pattern appearance can be regarded as an important criterion for measuring the interestingness of frequent patterns in several applications. A …
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