Finding Informative Rules in Interval Sequences

Höppner, Frank; Klawonn, Frank

doi:10.1007/3-540-44816-0_13

Cited by 54 publications

(49 citation statements)

References 5 publications

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“…Correlations between the interval-based events or any possible association rules however, are not being considered. (Hoeppner, 2001;Mooney & Roddick, 2004;Hoeppner & Klawonn, 2001) employ apriori-based techniques to find temporal patterns that occur frequently in the input event sequence. Along with the frequent patterns, they extract association rules and the latest applies some interestingness measures to evaluate their significance.…”

Section: Temporal Mining and Association Rulesmentioning

confidence: 99%

“…Association rules among items that belong to a frequent itemset are defined in (Srikant & Agrawal, 1996;Agrawal & Srikant, 1994). Similar definitions are given in (Harms et al, 2002) for sequence association rules, and in (Hoeppner, 2001;Hoeppner & Klawonn, 2001) for association rules among interval-based events. In the above works, the evaluation of the rules is achieved by the usage of interestingness measures.…”

Section: Temporal Mining and Association Rulesmentioning

confidence: 99%

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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%

Section: Temporal Mining and Association Rulesmentioning

confidence: 99%

Mining frequent arrangements of temporal intervals

et al. 2009

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“…above is referred to as the maximality assumption [4]. The maximality assumption guarantees that each temporal interval A is maximal, in the sense that there is no other temporal interval in the sequence sharing a time with A and carrying the same label.…”

Section: Equationmentioning

confidence: 99%

“…Previous investigations on discovering patterns from sequences of temporal intervals include the work of [4] and Papapetrou et al [6]. Both approach the problem very differently from each other.…”

Section: A New Support Definitionmentioning

confidence: 99%

Mining Sequences of Temporal Intervals

Kempe

Hipp

2006

Lecture Notes in Computer Science

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Abstract.Recently a new type of data source came into the focus of knowledge discovery from temporal data: interval sequences. In contrast to event sequences, interval sequences contain labeled events with a temporal extension. However, existing algorithms for mining patterns from interval sequences proved to be far from satisfying our needs. In brief, we missed an approach that at the same time: defines support as the number of pattern instances, allows input data that consists of more than one sequence, implements time constraints on a pattern instance, and counts multiple instances of a pattern within one interval sequence. In this paper we propose a new support definition which incorporates these properties. We also describe an algorithm that employs the new support definition and demonstrate its performance on field data from the automotive business.

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“…Of the existing attempts to tackle this problem, many rely on scanning the data and counting the occurrence of every legal antecedent and consequent (for example, Mannila, Toivonen, & Verkamo, 1997;Agrawal & Srikant, 1995;Höppner & Klawonn, 2001). The rules are then ranked according to some measure of interestingness.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Rule Mining in Time Series Databases

Hetland

Sætrom²

2005

Mach Learn

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Abstract. Data mining in the form of rule discovery is a growing field of investigation. A recent addition to this field is the use of evolutionary algorithms in the mining process. While this has been used extensively in the traditional mining of relational databases, it has hardly, if at all, been used in mining sequences and time series. In this paper we describe our method for evolutionary sequence mining, using a specialized piece of hardware for rule evaluation, and show how the method can be applied to several different mining tasks, such as supervised sequence prediction, unsupervised mining of interesting rules, discovering connections between separate time series, and investigating tradeoffs between contradictory objectives by using multiobjective evolution.

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Finding Informative Rules in Interval Sequences

Cited by 54 publications

References 5 publications

Mining frequent arrangements of temporal intervals

Mining frequent arrangements of temporal intervals

Mining Sequences of Temporal Intervals

Evolutionary Rule Mining in Time Series Databases

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