Ramakrishnan Srikant and Rakesh Agrawal. Mining sequential patterns: Generalizations and performance improvements. In EDBT, Avignon, France, Mar. 1996. Home/Search Document Not in Database Summary Related Articles Check |
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Constraint Based Mining of First Order Sequences in SeqLog.. - Lee, De Raedt (Correct)
....in the domains of usermodeling that validate of the approach. 1 Introduction Data mining has been a hot research topic in recent years, and the mining of knowledge from data of various models has been studied. One popular data model that has attracted a lot of attention concerns sequential data [2, 20, 13, 6, 21, 22]. Many of these approaches are extensions of the classical level wise itemset discovery algorithm Apriori [1] However, the data models that have been used so far for modeling sequential patterns are not very expressive and often based on some form of propositional logic. The need for more ....
Ramakrishnan Srikant and Rakesh Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Peter M. G. Apers, Mokrane Bouzeghoub, and Georges Gardarin, editors, Proc. 5th Int. Conf. Extending Database Technology, EDBT, volume 1057, pages 3--17. Springer-Verlag, 25-- 29 1996.
Authorship Identification for Heterogeneous Documents - Tsuboi (2002) (1 citation) (Correct)
....we denote item as an element of a sequence. 5.2 The Pre xSpan Algorithm Most conventional methods for mining sequential patterns are based on Apriori [1] property, which states that any super pattern of a non frequent pattern cannot be frequent. These Apriori like methods such as GSP [19] adopt the candidate generation and test approach. In this approach, each subsequent pass 13 generates candidate sequences, counts their supports by scanning a sequential database, and prunes the candidates whose support is less than the minimum support threshold. The next pass generate new ....
Ramakrishnan Srikant and Rakesh Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Peter M. G. Apers, Mokrane Bouzeghoub, and Georges Gardarin, editors, Proc. 5th Int. Conf. Extending Database Technology, EDBT, volume 1057, pages 3-17. SpringerVerlag, 25-29 1996.
A GSP-based Efficient Algorithm for Mining Frequent Sequences - Zhang, Kao, Yip, Cheung (2002) (Correct)
....Minghua Zhang Ben Kao Chi Lap Yip David Cheung Department of Computer Science and Information Systems, The University of Hong Kong, Hong Kong. rahzhang, kao, clyip, dcheung csis.hku. hk Abstract This paper studies the problem of mining frequent sequences in transactional databases. In [3], Agrawal and Srikant proposed the GSP algorithm for extracting frequently occurring sequences. GSP is an iterative algorithm. It scans the database a number of times depending on the length of the longest frequent sequences in the database. The I O cost is thus substantial if the database ....
....the order of transactions a customer has conducted. Roughly speaking, the problem of mining frequent sequences is to discover ubsequences (of itemsets) that occur frequently enough among all the customer sequences. Based on this model, several algorithms have been proposed. Among them, GSP [3] is a very efficient one. GSP is a multi phase iterative algorithm. It scans the database a number of times. During the i th iteration, frequent sequences of length i are discovered. The number of database scans GSP requires is thus determined by the length of the longest frequent sequences in the ....
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Ramakrishnan Srikant and Rakesh Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proc. of the 5th Conference on Extending Database Technology (EDBT), Avignion, France, March 1996. 5
Modeling Interactions based on Consistent Patterns - Srinath Srinivasa.. (1999) (Correct)
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Ramakrishnan Srikant and Rakesh Agrawal. Mining sequential patterns: Generalizations and performance improvements. In EDBT, Avignon, France, Mar. 1996.
Anomaly Detection Using Data Mining - Singh (1999) (Correct)
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Ramakrishnan Srikant and Rakesh Agrawal "Mining Sequential Patterns: Generalizations and Performance Improvements" Proc. 5th Int. Conf. Extending Database Technology, EDBT, 1996.
Constraint Based Mining of First Order - Sequences In Seqlog (Correct)
No context found.
Ramakrishnan Srikant and Rakesh Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Peter M. G. Apers, Mokrane Bouzeghoub, and Georges Gardarin, editors, Proc. 5th Int. Conf. Extending Database Technology, EDBT, volume 1057, pages 3--17. Springer-Verlag, 25-- 29 1996.
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