Closed Multidimensional Sequential Pattern Mining

Songram, Panida; Boonjing, Veera; Intakosum, Sarun

doi:10.1109/itng.2006.41

Cited by 18 publications

(20 citation statements)

References 10 publications

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“…These have been previously applied, for example, to analyze earthquake data [10] and source code in software engineering [24]. While traditional sequential pattern mining (SPM) algorithms have as their only goal to discover sequential patterns that occur frequently in several transactions of a database [13], other algorithms have proposed numerous extensions to the problem of sequential pattern mining such as mining patterns respecting time-constraints [10], mining compact representations of patterns [14,16,24], and incremental mining of patterns [24].…”

Section: B Step 2: Mining a Partial Task Model From Users' Plansmentioning

confidence: 99%

“…For this work, we developed a custom sequential pattern mining algorithm [15] that combines several features from other algorithms such as accepting time constraints [10], processing databases with dimensional information [11], and mining a compact representation of all patterns [14,16], and that also adds some original features such as accepting symbols with parameter values. We have built this algorithm to address the type of data to be recorded in a tutoring system offering procedural exercises such as CanadarmTutor.…”

Section: B Step 2: Mining a Partial Task Model From Users' Plansmentioning

confidence: 99%

“…[14,16,24] are sequences that are not contained in another sequence having the same support. Mining frequent closed sequences has the advantage of greatly reducing the number of patterns found, without information loss (the set of closed frequent sequences allows reconstituting the set of all frequent sequences and their support) [14].…”

Section: B Step 2: Mining a Partial Task Model From Users' Plansmentioning

confidence: 99%

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Lessons from Project LISTEN’s Session Browser

Mostow¹,

Beck²,

Cuneo³

et al. 2010

Handbook of Educational Data Mining

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Section: B Step 2: Mining a Partial Task Model From Users' Plansmentioning

confidence: 99%

Section: B Step 2: Mining a Partial Task Model From Users' Plansmentioning

confidence: 99%

Section: B Step 2: Mining a Partial Task Model From Users' Plansmentioning

confidence: 99%

See 1 more Smart Citation

Lessons from Project LISTEN’s Session Browser

Mostow¹,

Beck²,

Cuneo³

et al. 2010

Handbook of Educational Data Mining

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“…For this work, we chose a sequential pattern mining algorithm that we have developed [8], as it combines several features from other algorithms such as accepting time constraints [11], processing databases with dimensional information [17], eliminating redundancy [20,18], and also because it offers some original features such as accepting symbols with numeric values [8]. We describe next some basic features of the algorithm.…”

Section: Mining Temporal Patterns From Sequences Of Eventsmentioning

confidence: 99%

“…Mining frequent closed sequences has the advantage of greatly reducing the size of patterns found, without information loss (the set of closed frequent sequences allows reconstituting the set of all frequent sequences and their support) [20]. To mine only frequent closed sequences, our sequential pattern mining algorithm was extended based on [20] and [18] to mine closed MD-Sequences (see [8]). …”

Section: The Learning Phasementioning

confidence: 99%

Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies

Fournier-Viger

Nkambou

Faghihi

et al. 2009

Data Mining and Multi-Agent Integration

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Abstract. We propose two mechanisms for agent learning based on the idea of mining temporal patterns from agent behavior. The first one consists of extracting temporal patterns from the perceived behavior of other agents accomplishing a task, to learn the task. The second learning mechanism consists in extracting temporal patterns from an agent's own behavior. In this case, the agent then reuses patterns that brought self-satisfaction. In both cases, no assumption is made on how the observed agents' behavior is internally generated. A case study with a real application is presented to illustrate each learning mechanism.

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OLAP-Sequential Mining: Summarizing Trends from Historical

Plantevit

Laurent

Teisseire

2008

Annals of Information Systems

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Data warehouses are now well recognized as the way to store historical data that will then be available for future queries and analysis. In this context, some challenges are still open, among which the problem of mining such data. OLAP mining, introduced by J. Han in 1997, aims at coupling data mining techniques and data warehousing. These techniques have to take the specificities of such data into account. One of the specificities that is often not addressed by classical methods for data mining is the fact that data warehouses describe data through several dimensions. Moreover, the data are stored through time, and we thus argue that sequential patterns are one of the best ways to summarize the trends from such databases. Sequential pattern mining aims at discovering correlations among events through time. However, the number of patterns can become very important when taking several analysis dimensions into account, as it is the case in the framework of multidimensional databases. This is why we propose here to define a condensed representation without loss of information: closed multidimensional sequential patterns. This representation introduces properties that allow to deeply prune the search space. In this paper, we also define algorithms that do not require candidate set maintenance. Experiments on synthetic and real data are reported and emphasize the interest of our proposal.

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Closed Multidimensional Sequential Pattern Mining

Cited by 18 publications

References 10 publications

Lessons from Project LISTEN’s Session Browser

Lessons from Project LISTEN’s Session Browser

Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies

OLAP-Sequential Mining: Summarizing Trends from Historical

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