Frequent Itemset Mining Task
(Redirected from Frequent Items Finding Task)
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A Frequent Itemset Mining Task is a frequent-pattern mining task that requires the identification of frequent item sets in iid datasets.
- Context:
- Input: a Database (typically a fully-structured database).
- output: a Frequent Itemset Set.
- It can be solved by a Frequent Items Finding System (that implements a Frequent Items Finding Algorithm/Frequent-Itemset Mining Algorithm.
- …
- Example(s):
- Counter-Example(s):
- See: Finding Task, Frequent Items, Frequent Itemset Finding Task.
References
2012
- http://en.wikipedia.org/wiki/Association_rule_learning#Other_types_of_association_mining
- Contrast set learning is a form of associative learning. Contrast set learners use rules that differ meaningfully in their distribution across subsets.[1]
- Weighted class learning is another form of associative learning in which weight may be assigned to classes to give focus to a particular issue of concern for the consumer of the data mining results.
- K-optimal pattern discovery provides an alternative to the standard approach to association rule learning that requires that each pattern appear frequently in the data.
- Mining frequent sequences uses support to find sequences in temporal data.[2]
- Generalized Association Rules hierarchical taxonomy (concept hierarchy)
- Quantitative Association Rules categorical and quantitative data [3]
- Interval Data Association Rules e.g. partition the age into 5-year-increment ranged
- Maximal Association Rules
- Sequential Association Rules temporal data e.g. first buy computer, then CD-Roms, then a webcam.
- ↑ Menzies, Tim; and Hu, Ying; Data Mining for Very Busy People, IEEE Computer, October 2003, pp. 18-25
- ↑ Zaki, Mohammed J. (2001); SPADE: An Efficient Algorithm for Mining Frequent Sequences, Machine Learning Journal, 42, pp. 31–60
- ↑ Salleb-Aouissi, Ansaf; Vrain, Christel; and Nortet, Cyril (2007). "QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules". International Joint Conference on Artificial Intelligence (IJCAI): 1035–1040.
2009
- (Cormode & Hadjieleftheriou) ⇒ Graham Cormode, and Marios Hadjieleftheriou. (2009). “Finding the Frequent Items in Streams of Data.” In: Communications of the ACM, 52(10). doi:10.1145/1562764.1562789
- QUOTE: The frequent items problem is to process a stream of items and find all those which occur more than a given fraction of the time. It is one of the most heavily studied problems in mining data streams, dating back to the 1980s. Many other applications rely directly or indirectly on finding the frequent items, and implementations are in use in large-scale industrial systems. In this paper, we describe the most important algorithms for this problem in a common framework. We place the different solutions in their historical context, and describe the connections between them, with the aim of clarifying some of the confusion that has surrounded their properties.
2007
- (Han et al., 2007) ⇒ Jiawei Han, Hong Cheng, Dong Xin, and Xifeng Yan. (2007). “Frequent Pattern Mining: current status and future directions.” In: Data Mining and Knowledge Discovery, 15(1). doi:10.1007/s10618-006-0059-1
2004
- http://fimi.ua.ac.be/ Frequent Itemset Mining Implementations Repository
1993
- (Agrawal et al., 1993) ⇒ Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. (1993). “Mining Association Rules Between Sets of Items in Large Databases.” In: Proceedings of ACM SIGMOD Conference (SIGMOD 1993). do>10.1145/170035.170072.