Incremental Meta-Mining from Large Temporal Data Sets

Abraham, Tamas; Roddick, John F.

doi:10.1007/978-3-540-49121-7_4

Cited by 24 publications

(11 citation statements)

References 2 publications

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“…It is argued that reducing the temporal granularity can lead to the extraction of more interesting rules. In a same fashion, (Chen & Petrounias, 1999;Abraham & Roddick, 1999) consider the discovery of association rules in temporal databases and thus the extraction of temporal features of associated items. The support of the rules is measured only during these intervals.…”

Section: Temporal Mining and Association Rulesmentioning

confidence: 99%

Mining frequent arrangements of temporal intervals

et al. 2009

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Abstract. The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Three efficient methods to find frequent arrangements of temporal intervals are described; the first two are tree-based and use breadth and depth first search to mine the set of frequent arrangements, whereas the third one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.

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Section: Temporal Mining and Association Rulesmentioning

confidence: 99%

Mining frequent arrangements of temporal intervals

et al. 2009

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“…Its main characteristic is generation of data models, called meta-models, from the already generated data models (referred to as meta-data) [72], [76]. Researchers argue that such meta-models are more suitable for describing knowledge that can be considered interesting, and that reduced complexity of generated rule sets can be achieved [1], [50], [72], [76]. Finally, parallelization for cluster systems will also be a topic of future research.…”

Section: A Future Workmentioning

confidence: 99%

Highly scalable and robust rule learner: performance evaluation and comparison

Kurgan

Cios

Dick

2006

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Business intelligence and bioinformatics applications increasingly require the mining of datasets consisting of millions of data points, or crafting real-time enterprise-level decision support systems for large corporations and drug companies. In all cases, there needs to be an underlying data mining system, and this mining system must be highly scalable. To this end, we describe a new rule learner called DataSqueezer. The learner belongs to the family of inductive supervised rule extraction algorithms. DataSqueezer is a simple, greedy, rule builder that generates a set of production rules from labeled input data. In spite of its relative simplicity, DataSqueezer is a very effective learner. The rules generated by the algorithm are compact, comprehensible, and have accuracy comparable to rules generated by other state-of-the-art rule extraction algorithms. The main advantages of DataSqueezer are very high efficiency, and missing data resistance. DataSqueezer exhibits log-linear asymptotic complexity with the number of training examples, and it is faster than other state-ofthe-art rule learners. The learner is also robust to large quantities of missing data, as verified by extensive experimental comparison with the other learners. DataSqueezer is thus well suited to modern data mining and business intelligence tasks, which commonly involve huge datasets with a large fraction of missing data.

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“…The approach proposed in this paper is different from the approaches presented in the literature (Abraham and Roddick, 1999, Koperski, 1999, Mennis and Liu, 2005, Tsoukatos and Gunopulos, 2001) with respect to the specific data mining problem addressed, and mainly the use of multi-granularity concept to both be able to design scalable technique for data mining and analysis and speed up the process of the mining and its accuracy. The work presented in (Tsoukatos and Gunopulos, 2001) focuses on mining frequent patterns, while (Abraham and Roddick, 1999, Koperski, 1999, Mennis and Liu, 2005 address the mining of association rules, meta-rules and classification.…”

Section: Resultsmentioning

confidence: 95%

“…The work presented in (Tsoukatos and Gunopulos, 2001) focuses on mining frequent patterns, while (Abraham and Roddick, 1999, Koperski, 1999, Mennis and Liu, 2005 address the mining of association rules, meta-rules and classification. (Tsoukatos and Gunopulos, 2001) uses spatial granularities defined according to boundary regions, and the operator supported to perform granularity conversions is region merge.…”

Section: Resultsmentioning

confidence: 99%

“…(Tsoukatos and Gunopulos, 2001) uses spatial granularities defined according to boundary regions, and the operator supported to perform granularity conversions is region merge. Likewise, only spatial concept hierarchy are supported in (Abraham and Roddick, 1999, Koperski, 1999, Mennis and Liu, 2005. In all these projects, spatial granularities are employed for rules representations.…”

Section: Resultsmentioning

confidence: 99%

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Mining Spatio-Temporal Data at Different Levels of Detail

Camossi

Bertolotto

Kechadi

Lecture Notes in Geoinformation and Cartography

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In this paper we propose a methodology for mining very large spatio-temporal datasets. We propose a two-pass strategy for mining and manipulating spatio-temporal datasets at different levels of detail (i.e., granularities). The approach takes advantage of the multi-granular capability of the underlying spatio-temporal model to reduce the amount of data that can be accessed initially. The approach is implemented and applied to real-world spatio-temporal datasets. We show that the technique can deal easily with very large datasets without losing the accuracy of the extracted patterns, as demonstrated in the experimental results.

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Incremental Meta-Mining from Large Temporal Data Sets

Cited by 24 publications

References 2 publications

Mining frequent arrangements of temporal intervals

Mining frequent arrangements of temporal intervals

Highly scalable and robust rule learner: performance evaluation and comparison

Mining Spatio-Temporal Data at Different Levels of Detail

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